{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Batch Normalization\n",
    "One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. \n",
    "One idea along these lines is batch normalization which was proposed by [1] in 2015.\n",
    "\n",
    "The idea is relatively straightforward. Machine learning methods tend to work better when their input data consists of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features; this will ensure that the first layer of the network sees data that follows a nice distribution. However, even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n",
    "\n",
    "The authors of [1] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, [1] proposes to insert batch normalization layers into the network. At training time, a batch normalization layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n",
    "\n",
    "It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n",
    "\n",
    "[1] [Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n",
    "Internal Covariate Shift\", ICML 2015.](https://arxiv.org/abs/1502.03167)\n",
    "\n",
    "--- \n",
    "BN 的作用：对每一层的输入进行归一化，防止激活层梯度消失"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\" returns relative error \"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))\n",
    "\n",
    "def print_mean_std(x,axis=0):\n",
    "    print('  means: ', x.mean(axis=axis))\n",
    "    print('  stds:  ', x.std(axis=axis))\n",
    "    print() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:  (49000, 3, 32, 32)\n",
      "y_train:  (49000,)\n",
      "X_val:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "  print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: forward\n",
    "In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation.\n",
    "\n",
    "Referencing the paper linked to above in [1] may be helpful!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before batch normalization:\n",
      "  means:  [ -2.3814598  -13.18038246   1.91780462]\n",
      "  stds:   [27.18502186 34.21455511 37.68611762]\n",
      "\n",
      "After batch normalization (gamma=1, beta=0)\n",
      "  means:  [2.22044605e-17 8.38218384e-17 4.46864767e-17]\n",
      "  stds:   [0.99999999 1.         1.        ]\n",
      "\n",
      "After batch normalization (gamma= [1. 2. 3.] , beta= [11. 12. 13.] )\n",
      "  means:  [11. 12. 13.]\n",
      "  stds:   [0.99999999 1.99999999 2.99999999]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after batch normalization   \n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before batch normalization:')\n",
    "print_mean_std(a,axis=0)\n",
    "\n",
    "gamma = np.ones((D3,))\n",
    "beta = np.zeros((D3,))\n",
    "# Means should be close to zero and stds close to one\n",
    "print('After batch normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=0)\n",
    "\n",
    "gamma = np.asarray([1.0, 2.0, 3.0])\n",
    "beta = np.asarray([11.0, 12.0, 13.0])\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "print('After batch normalization (gamma=', gamma, ', beta=', beta, ')')\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After batch normalization (test-time):\n",
      "  means:  [-0.03927354 -0.04349152 -0.10452688]\n",
      "  stds:   [1.01531428 1.01238373 0.97819988]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "\n",
    "for t in range(50):\n",
    "  X = np.random.randn(N, D1)\n",
    "  a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "  batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "bn_param['mode'] = 'test'\n",
    "X = np.random.randn(N, D1)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print('After batch normalization (test-time):')\n",
    "print_mean_std(a_norm,axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: backward\n",
    "Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n",
    "\n",
    "To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n",
    "\n",
    "Once you have finished, run the following to numerically check your backward pass.\n",
    "\n",
    "---\n",
    "$$\n",
    "\\hat{x}_i=\\dfrac{x_i-\\mu}{\\sqrt{\\sigma^2+\\epsilon}} \\\\\n",
    "y_i = \\gamma\\hat{x}_i+\\beta\\\\\n",
    "$$\n",
    "\n",
    "$\\dfrac{\\partial\\sigma^2}{\\partial\\mu}=\\dfrac{2}{N}\\sum(\\mu-x_i)=0$ 导致我以为 $\\dfrac{\\partial l}{\\partial\\mu}=\\dfrac{\\partial l}{\\partial\\sigma^2}\\dfrac{\\partial\\sigma^2}{\\partial\\mu}=0$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  1.7029275364845641e-09\n",
      "dgamma error:  7.420414216247087e-13\n",
      "dbeta error:  2.8795057655839487e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: batchnorm_forward(x, a, beta, bn_param)[0]\n",
    "fb = lambda b: batchnorm_forward(x, gamma, b, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n",
    "#You should expect to see relative errors between 1e-13 and 1e-8\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: alternative backward\n",
    "In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For example, you can derive a very simple formula for the sigmoid function's backward pass by simplifying gradients on paper.\n",
    "\n",
    "Surprisingly, it turns out that you can do a similar simplification for the batch normalization backward pass too!  \n",
    "\n",
    "In the forward pass, given a set of inputs $X=\\begin{bmatrix}x_1\\\\x_2\\\\...\\\\x_N\\end{bmatrix}$, \n",
    "\n",
    "we first calculate the mean $\\mu$ and variance $v$.\n",
    "With $\\mu$ and $v$ calculated, we can calculate the standard deviation $\\sigma$  and normalized data $Y$.\n",
    "The equations and graph illustration below describe the computation ($y_i$ is the i-th element of the vector $Y$).\n",
    "\n",
    "\\begin{align}\n",
    "& \\mu=\\frac{1}{N}\\sum_{k=1}^N x_k  &  v=\\frac{1}{N}\\sum_{k=1}^N (x_k-\\mu)^2 \\\\\n",
    "& \\sigma=\\sqrt{v+\\epsilon}         &  y_i=\\frac{x_i-\\mu}{\\sigma}\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"notebook_images/batchnorm_graph.png\" width=691 height=202>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "The meat of our problem during backpropagation is to compute $\\frac{\\partial L}{\\partial X}$, given the upstream gradient we receive, $\\frac{\\partial L}{\\partial Y}.$ To do this, recall the chain rule in calculus gives us $\\frac{\\partial L}{\\partial X} = \\frac{\\partial L}{\\partial Y} \\cdot \\frac{\\partial Y}{\\partial X}$.\n",
    "\n",
    "The unknown/hart part is $\\frac{\\partial Y}{\\partial X}$. We can find this by first deriving step-by-step our local gradients at \n",
    "$\\frac{\\partial v}{\\partial X}$, $\\frac{\\partial \\mu}{\\partial X}$,\n",
    "$\\frac{\\partial \\sigma}{\\partial v}$, \n",
    "$\\frac{\\partial Y}{\\partial \\sigma}$, and $\\frac{\\partial Y}{\\partial \\mu}$,\n",
    "and then use the chain rule to compose these gradients (which appear in the form of vectors!) appropriately to compute $\\frac{\\partial Y}{\\partial X}$.\n",
    "\n",
    "If it's challenging to directly reason about the gradients over $X$ and $Y$ which require matrix multiplication, try reasoning about the gradients in terms of individual elements $x_i$ and $y_i$ first: in that case, you will need to come up with the derivations for $\\frac{\\partial L}{\\partial x_i}$, by relying on the Chain Rule to first calculate the intermediate $\\frac{\\partial \\mu}{\\partial x_i}, \\frac{\\partial v}{\\partial x_i}, \\frac{\\partial \\sigma}{\\partial x_i},$ then assemble these pieces to calculate $\\frac{\\partial y_i}{\\partial x_i}$. \n",
    "\n",
    "You should make sure each of the intermediary gradient derivations are all as simplified as possible, for ease of implementation. \n",
    "\n",
    "After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx difference:  0.0\n",
      "dgamma difference:  0.0\n",
      "dbeta difference:  0.0\n",
      "speedup: 0.86x\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D = 100, 500\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "t1 = time.time()\n",
    "dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n",
    "t2 = time.time()\n",
    "dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n",
    "t3 = time.time()\n",
    "\n",
    "print('dx difference: ', rel_error(dx1, dx2))\n",
    "print('dgamma difference: ', rel_error(dgamma1, dgamma2))\n",
    "print('dbeta difference: ', rel_error(dbeta1, dbeta2))\n",
    "print('speedup: %.2fx' % ((t2 - t1) / (t3 - t2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fully Connected Nets with Batch Normalization\n",
    "Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs231n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n",
    "\n",
    "Concretely, when the `normalization` flag is set to `\"batchnorm\"` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n",
    "\n",
    "HINT: You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`. If you decide to do so, do it in the file `cs231n/classifiers/fc_net.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.2611955101340957\n",
      "W1 relative error: 1.10e-04\n",
      "W2 relative error: 2.85e-06\n",
      "W3 relative error: 3.92e-10\n",
      "b1 relative error: 8.33e-09\n",
      "b2 relative error: 2.00e-07\n",
      "b3 relative error: 4.78e-11\n",
      "beta1 relative error: 7.33e-09\n",
      "beta2 relative error: 1.89e-09\n",
      "gamma1 relative error: 7.57e-09\n",
      "gamma2 relative error: 1.96e-09\n",
      "\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  6.996533220108303\n",
      "W1 relative error: 1.98e-06\n",
      "W2 relative error: 2.28e-06\n",
      "W3 relative error: 1.11e-08\n",
      "b1 relative error: 1.11e-08\n",
      "b2 relative error: 2.36e-08\n",
      "b3 relative error: 2.23e-10\n",
      "beta1 relative error: 6.65e-09\n",
      "beta2 relative error: 3.48e-09\n",
      "gamma1 relative error: 5.94e-09\n",
      "gamma2 relative error: 4.14e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "# You should expect losses between 1e-4~1e-10 for W, \n",
    "# losses between 1e-08~1e-10 for b,\n",
    "# and losses between 1e-08~1e-09 for beta and gammas.\n",
    "for reg in [0, 3.14]:\n",
    "  print('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64,\n",
    "                            normalization='batchnorm')\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n",
    "  if reg == 0: print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batchnorm for deep networks\n",
    "Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solver with batch norm:\n",
      "(Iteration 1 / 200) loss: 2.340974\n",
      "(Epoch 0 / 10) train acc: 0.107000; val_acc: 0.115000\n",
      "(Epoch 1 / 10) train acc: 0.314000; val_acc: 0.267000\n",
      "(Iteration 21 / 200) loss: 2.039365\n",
      "(Epoch 2 / 10) train acc: 0.390000; val_acc: 0.279000\n",
      "(Iteration 41 / 200) loss: 2.036710\n",
      "(Epoch 3 / 10) train acc: 0.497000; val_acc: 0.316000\n",
      "(Iteration 61 / 200) loss: 1.769763\n",
      "(Epoch 4 / 10) train acc: 0.532000; val_acc: 0.310000\n",
      "(Iteration 81 / 200) loss: 1.268883\n",
      "(Epoch 5 / 10) train acc: 0.600000; val_acc: 0.325000\n",
      "(Iteration 101 / 200) loss: 1.263426\n",
      "(Epoch 6 / 10) train acc: 0.633000; val_acc: 0.318000\n",
      "(Iteration 121 / 200) loss: 1.108584\n",
      "(Epoch 7 / 10) train acc: 0.690000; val_acc: 0.342000\n",
      "(Iteration 141 / 200) loss: 1.176694\n",
      "(Epoch 8 / 10) train acc: 0.721000; val_acc: 0.317000\n",
      "(Iteration 161 / 200) loss: 0.682344\n",
      "(Epoch 9 / 10) train acc: 0.767000; val_acc: 0.317000\n",
      "(Iteration 181 / 200) loss: 0.888016\n",
      "(Epoch 10 / 10) train acc: 0.807000; val_acc: 0.336000\n",
      "\n",
      "Solver without batch norm:\n",
      "(Iteration 1 / 200) loss: 2.302332\n",
      "(Epoch 0 / 10) train acc: 0.129000; val_acc: 0.131000\n",
      "(Epoch 1 / 10) train acc: 0.283000; val_acc: 0.250000\n",
      "(Iteration 21 / 200) loss: 2.041970\n",
      "(Epoch 2 / 10) train acc: 0.316000; val_acc: 0.277000\n",
      "(Iteration 41 / 200) loss: 1.900473\n",
      "(Epoch 3 / 10) train acc: 0.373000; val_acc: 0.282000\n",
      "(Iteration 61 / 200) loss: 1.713156\n",
      "(Epoch 4 / 10) train acc: 0.390000; val_acc: 0.310000\n",
      "(Iteration 81 / 200) loss: 1.662209\n",
      "(Epoch 5 / 10) train acc: 0.434000; val_acc: 0.300000\n",
      "(Iteration 101 / 200) loss: 1.696062\n",
      "(Epoch 6 / 10) train acc: 0.536000; val_acc: 0.346000\n",
      "(Iteration 121 / 200) loss: 1.550785\n",
      "(Epoch 7 / 10) train acc: 0.530000; val_acc: 0.310000\n",
      "(Iteration 141 / 200) loss: 1.436308\n",
      "(Epoch 8 / 10) train acc: 0.622000; val_acc: 0.342000\n",
      "(Iteration 161 / 200) loss: 1.000868\n",
      "(Epoch 9 / 10) train acc: 0.654000; val_acc: 0.328000\n",
      "(Iteration 181 / 200) loss: 0.925456\n",
      "(Epoch 10 / 10) train acc: 0.728000; val_acc: 0.335000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [100, 100, 100, 100, 100]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 2e-2\n",
    "bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n",
    "model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "\n",
    "print('Solver with batch norm:')\n",
    "bn_solver = Solver(bn_model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True,print_every=20)\n",
    "bn_solver.train()\n",
    "\n",
    "print('\\nSolver without batch norm:')\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=20)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_training_history(title, label, baseline, bn_solvers, plot_fn, bl_marker='.', bn_marker='.', labels=None):\n",
    "    \"\"\"utility function for plotting training history\"\"\"\n",
    "    plt.title(title)\n",
    "    plt.xlabel(label)\n",
    "    bn_plots = [plot_fn(bn_solver) for bn_solver in bn_solvers]\n",
    "    bl_plot = plot_fn(baseline)\n",
    "    num_bn = len(bn_plots)\n",
    "    for i in range(num_bn):\n",
    "        label='with_norm'\n",
    "        if labels is not None:\n",
    "            label += str(labels[i])\n",
    "        plt.plot(bn_plots[i], bn_marker, label=label)\n",
    "    label='baseline'\n",
    "    if labels is not None:\n",
    "        label += str(labels[0])\n",
    "    plt.plot(bl_plot, bl_marker, label=label)\n",
    "    plt.legend(loc='lower center', ncol=num_bn+1) \n",
    "\n",
    "    \n",
    "plt.subplot(3, 1, 1)\n",
    "plot_training_history('Training loss','Iteration', solver, [bn_solver], \\\n",
    "                      lambda x: x.loss_history, bl_marker='o', bn_marker='o')\n",
    "plt.subplot(3, 1, 2)\n",
    "plot_training_history('Training accuracy','Epoch', solver, [bn_solver], \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-o', bn_marker='-o')\n",
    "plt.subplot(3, 1, 3)\n",
    "plot_training_history('Validation accuracy','Epoch', solver, [bn_solver], \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-o', bn_marker='-o')\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch normalization and initialization\n",
    "We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n",
    "\n",
    "The first cell will train 8-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running weight scale 1 / 20\n",
      "Running weight scale 2 / 20\n",
      "Running weight scale 3 / 20\n",
      "Running weight scale 4 / 20\n",
      "Running weight scale 5 / 20\n",
      "Running weight scale 6 / 20\n",
      "Running weight scale 7 / 20\n",
      "Running weight scale 8 / 20\n",
      "Running weight scale 9 / 20\n",
      "Running weight scale 10 / 20\n",
      "Running weight scale 11 / 20\n",
      "Running weight scale 12 / 20\n",
      "Running weight scale 13 / 20\n",
      "Running weight scale 14 / 20\n",
      "Running weight scale 15 / 20\n",
      "Running weight scale 16 / 20\n",
      "Running weight scale 17 / 20\n",
      "Running weight scale 18 / 20\n",
      "Running weight scale 19 / 20\n",
      "Running weight scale 20 / 20\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "bn_solvers_ws = {}\n",
    "solvers_ws = {}\n",
    "weight_scales = np.logspace(-4, 0, num=20)\n",
    "for i, weight_scale in enumerate(weight_scales):\n",
    "  print('Running weight scale %d / %d' % (i + 1, len(weight_scales)))\n",
    "  bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n",
    "  model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "\n",
    "  bn_solver = Solver(bn_model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "  bn_solver.train()\n",
    "  bn_solvers_ws[weight_scale] = bn_solver\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "  solver.train()\n",
    "  solvers_ws[weight_scale] = solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8+BQc2A7XvA9RpX0JWUREcmZsEZnC0a5dO7tw4UK/wxARESk6Vn4BE4dAtWYw6DMoWTHnYxa9D1/eDdVbwcAJUFpr+EVE8osxZpG1tl0w+4a0obyIiIgUUnHjYcKNUKMtDJ4cXBIIcOYN0P9j2PEbvHsJ7F4b0jBFROTkKBEUERGRYy0aDZOGQZ1z4LpJEF0ud8c37go3fAmHE+HdS2HzCaqNioiIL5QIioiIyFG/jIApd0LDLnDthJNf51ezHQyZDiVKwfvd4Y/peRuniIicEiWCIiIi4sx+Cab+AxpfAf3HQmTMqZ2vckOXDFZuBGP7wZIP8yZOERE5ZUoERUREijtrYcZT8N3D0Pwq6DsaIqLy5txlqsENX0H9C+CL22Hms+o1KCJSACgRFBERKc6sdQngzKeh9bXQ520Ij8zba0SVgQGfQMv+MONxV1U0LTVvryEiIrkSsj6CIiIiUsClp8M3/4T5I6HdEOj2HISF6DPiiBJw5VtQNhZmv+B6DV71LpQoGZrriYjICWlEUEREpDhKT4Mv/+6SwI53wBXPhy4JzGAMXPywSzhXT4UPesGhPaG9poiIZEmJoIiISHGTlgqfDYfFH8D598Olj7skLb+0Hwr9xsDWZa69xN4N+XdtEREBlAiKiIgUL6lHYOKNsHw8XPR/cNGD+ZsEZjijB1z/BRzc6RrPb12W/zGIiBRjSgRFRESKi5TD8Ml1sGoyXPYUnH+fv/HU6Qg3TYOwSHjvClg7w994RESKESWCIiIixcGRg/BxP/hjGnR/ETre5ndETtUmcPN0qFAHProa4sb7HZGISLGgRFBERKSoO7wPPrwa/vwJer8J7W7yO6JjlY2FG7+G2h1h0lDX2F69BkVEQkqJoIiISFGWtBfG9Ib4+a5dQ+uBfkeUtehycN2nrqH9dw/DNw+4yqYiIhIS6iMoIiJSVB3c5ZLAnauh7xho0s3viE4sIgr6vANlqsPc12D/VrhyJERG+x2ZiEiRo0RQRESkKNq/zfXp27sBBoyDhl38jig4YWFw2RMuGfz2P3BgJwwYCzEV/I5MRKRI0dRQERGRoiZhE7x3uft+3cTCkwQGOucON5V180IY1RUS4/2OSESkSFEiKCIiUpTsWQfvdYODu+H6z6HueX5HdPJaXO3WDe7bAu9cAttX+B2RiEiRoURQRESkqNj5u0sCjxyAwZOhVnu/Izp19c6Hm74BLIy6HP6c5XdEIiJFghJBERGRomDbr246aHoa3PAVxLb2O6K8U60ZDJkOZavDh33g10l+RyQiUugpERQRESnsNi+G0d0hvITrx1etqd8R5b3yteDGqVDjTJh4E8x70++IREQKNSWCIiIihdnGX1x10KgycNNUqNzI74hCp2RFGPQ5nNHd9Rn89kFIT/c7KhGRQkmJoIiISGH1508w5kooXRVu/AYq1PU7otCLjIZrRsNZQ2HOq/DZMEg94ndUIiKFjvoIioiIFEZ/fAefXAsV6sH1X0CZan5HlH/CwqHbs1CuBnz3CBzYAf3GQHQ5vyMTESk0NCIoIiJS2Kz6Ej7uD5VPd4VhilMSmMEYOO9uuHIEbPjZVUvdt9XvqERECg0lgiIiIoXJr5/C+OtdVdDBU6BUJb8j8ler/jBwPOxdD+9eAjtX+x2RiEihoERQRESksFjyEXx6M9TuAIM+g5jyfkdUMDTs4kZGU5Ph3UthxpPwYnN4pLz7Hjfe7whFRAocrREUEREpDBa8A1/dC/U7Q/+xUKKk3xEVLLGtYci3LhGc+czR7YmbYMqd7ueWff2JLZPPl2zm2Wmr2ZKQRGz5GO6/rDG929TwOywRKWY0IigiIlLQzX3dJYGnXw4DxikJzE7FehCexWfcKUmuqEwB8PmSzfxr0nI2JyRhgc0JSfxr0nI+X7LZ79BEpJjRiKCIiEhBEzcevn8MEuMhqiwkJ0LT3tDnbYgo4Xd0BVt2BWP2bYZn6kLF+ll8NXA9Co0JeXjPTltNUkraMduSUtJ4dtpqjQqKSL5SIigiIlKQxI13UxlTktzt5EQw4W40UElgzsrVdNNBM4suB82uhD3rYNMvsHwiYI/eH1XOjShmlSiWrppnSeKWhKRcbRcRCRUlgiIiIgXJ948dTQIz2DSY8Ti07u9PTIVJl4eOTaQBImOg23PHrhFMTYaEjS4xDPzauhRWfuEe87+OL+UlhVkkimWqQ1jwK20qlirB7oNHjttugfsmLGP4BfVpWLXMSfziIiK5o0RQRESkIEhLhd+nZj2aBW6aqOQsI9nLmFpbrqZLDjMXiomIgsqN3FdmaSlekvjnsUnijlWweiqkpwScJxoq1Ms6USxXE8LC/9p13rrdJCYdoVfYbO6PGE+s2cUWW5kXbX921O3Jl3FbmLgonkubVuPWCxvQpnaFEDxAIiKOsdbmvFch0K5dO7tw4UK/wxAREcmdg7tg8WhYMAr2xbtpoDbt+P3K1YK7f83/+ORY6WkuwTxmJNFLGPf+CamHj+4bFgkV6kLF+myPjOXtX6FhVCJXp31FRHryX7ulhkcT0etVdtfvxei5Gxg9Zz2JSSl0qF+RWy9syPmNKmPyYf2iiBR+xphF1tp2Qe2rRFBERMQHmxfB/Lddg/i0I1DvAjj7FkjeD1/edfzUxh6vFJj2B5KN9HTYv/W46aZJ29dgd6+lpEnO/tiARP9Acirj5m/k7Vnr2L4vmWaxZbn1wgZc3rw64WFKCEUke0oERURECqLUZFjxGcwf6RLBEqWh1QBoPxSqND66X2DV0OymNkqhsHLLPga8PY8yUeFMHNSA095uzTFFav5i4JGEY7Ykp6bxxZItvDVzLet2HaROpZLccn4D+rStQXRkeBbnEJHiTomgiIhIQZIYDwvfg0Xvw6FdUKkRtB8GrfpDdFm/o5MQ+WP7fvqNnEdURBjjb+lIrYol4cXmWa8DjSwF9/0OUaWPuyst3TJ95Tbe+HEtcfGJVCkTxZDz6nHt2bUpEx2ZD7+JiBQWSgRFRET8Zi2sn+1G/377Cmw6NL7cJYD1L8yXnnXin3U7D9Bv5DwAxt/SkXqVS7k7MrcHAQiLgPRUqNQQrh4F1VtleU5rLXPW7ubNH9cye80uykRHMKhDHW48tx5VykSF+lcSkUKgwCSCxpiuwMtAOPCOtfbpTPcPB24H0oADwDBr7Urvvn8BQ7z77rTWTjvRtZQIiohIgZB8AOI+cev/dq6CmArQ9npoNwQq1PE7OskHG3cfou+IuaSkpTNuWAcaVcvUDiKrqb9lqsOkoXBoN1z8KHS49YQfFsTFJ/DWzLVM/XUbJcLD6NuuFsPOr+9GHUWk2CoQiaAxJhz4HbgEiAcWAAMyEj1vn7LW2n3ezz2B26y1XY0xTYGPgfZALPAdcLq1WZVRc5QIioiIr3avhQXvwJKPXBP401q64i/Nr3LFXqRY2JyQRL8Rc9l/OJWPh3agaWwupv4e3A2T74DVX0OjS6H3m1Cq8gkPWbfzACNmrmPSknjSLXRvWZ3hFzTgjOqacixSHOUmEQxlH8H2wBpr7TovqHFAL+CvRDAjCfSU4ujq6V7AOGttMvCnMWaNd765IYxXREQkd9LTYc10N/1zzXeuXUCz3m76Z82zNP2zmNm+7zDXvj2PxEMpfDT07NwlgQClKkH/se4DhWn/gTfPgT4j3VTibNSvUppnrm7J3Zeczruz1zH2l418sXQLnRtX4dYLG9K+XsVT+p1EpOgKZSJYAwhcDR0PnJ15J2PM7cA9QAngooBj52U6tkYWxw4DhgHUrl07T4IWERHJ0aE9sPQj94Z973o3ra/zf6DtYChTze/oxAe7DiQz8O157NifzJghZ9OyZvmTO5Exrops7Q4w8Sb4oDecd5d7foVnXxjmtHLR/OeKptzeuSFj5m7gvTnr6TtiLu3qVODWCxvQuXFVwtR6QkQChHJq6DXAZdbam73bg4D21tq/ZbP/QG//wcaY14G51toPvfveBb621n6a3fU0NVREREJu23I3+hc3AVKToPY57k37GT1O+CZdira9B48w4O15rN99kPdvbE+H+pXy5sRHDsI3D8DiD6DGmXDVu1CxXlCHJh1JY/zCTYz8aR2bE5JoXK0Mwy+sT/eWsUSGh+VNfCJS4BSUqaHxQK2A2zWBLSfYfxzw5kkeKyIiEhppKbBqiiv+snEORMS4nn7th8JpLfyOTnyWmJTCoFG/sG7XQUYNPivvkkCAEqWg56tQvzNMuQtGnA/dX4QWV+d4aEyJcAafU5eBZ9dmyjLXi/DuT5bx3LTfGXZ+ffq2q0VMCfUiFCnOQjkiGIErFtMF2IwrFjPQWrsiYJ9G1to/vJ97AA9ba9sZY5oBYzlaLOZ7oJGKxYiISL7Zv931/Vs4Cg5sgwp14ayh0OZaVwlUir0Dyalc984vrNiSyIhBZ3JRkxBOC967AT69GeLnQ+vroNv/XKIYpPR0yw+/7eCNH9eweGMCFUuV4MZz6nJ9x7qUK6nRbJGiokBUDfUC6Qa8hGsfMcpa+4Qx5jFgobV2sjHmZeBiIAXYC9yRkSgaY/4D3ASkAndZa6ee6FpKBEVE5JRZC5vmu+mfK7+A9BRoeLEr/tLwYgjTCIo4h46kcsOoBSzauJfXB7ala/PTQn/RtFSY+TT89JzXc/DdbHsOZsday4L1e3nzxzXMWL2TUiXCubZDHYacV49qZaNDFLiI5JcCkwjmJyWCIiIStMx93C58wG2fPxK2LoOocm7k76yboVIDf2OVAudwShpDRi9g7trdvNy/DT1axeZvAH/+BJOGuZ6DlzwGZw8/qQq1K7fs462Za/kybgsRYWH0aVuDYefXJy4+kWenrWZLQhKx5WO4/7LG9G5zXM0+ESmAlAiKiIhkJ248TLkTUpKOv69qU7f2r0VfiCqd/7FJgZecmsbwMYv48fedPHd1K646s6Y/gRzcDV/cDr9PhUaXQe83cuw5mJ2Nuw8xctZaxi+M50hqOmEG0gPeHsZEhvNUnxZKBkUKASWCIiIi2XmxOSRuOn57qSpw3x/q/SfZSklL5/aPFvPtyu08eWULBp7tc+sqa10Ro28fdOtW+4w4Yc/BnOzcn0zn537kQHLqcffVKB/Dzw9clMVRIlKQ5CYRVP1gEREpHtJSXNuHrJJAgIO7lARKtlLT0rnrk6V8u3I7j/Ro6n8SCO75evYwGPo9RJd1PQe/e9Q9109ClTJRHMwiCQTYkpDFCLqIFGpKBEVEpGhLSoCfX4aXW8GkmyEsm85J5Xya4icFXnq65R8T4/gqbiv/7taEG84NrpdfvjmtBQz7EdoOgtkvwKiusHf9SZ0qtnxMrraLSOGlRFBERIqmPX/C1H/CC01h+kOu6MvA8dDrDYjM9KY2Mga6PORPnFKgpadb/v3ZciYt2cy9l5zOsPMLaPGgjJ6DV78Hu/6AtzrB8om5Ps39lzUmJvLY6rgxkWHcf1njvIpURAqIUDaUFxERyV/WwqZfYO5r8NtXYMJd8+0Ot0H1lkf3M+bYqqFdHnJN4kUCWGt5dMoKxi3YxB2dG/K3Lo38DilnzftAjTPh0yHua+2MXPUczCgI8+y01Wz2poPe1rmhCsWIFEFKBEVEpPBLS4VVX8Dc12HzIoguD+fd7RrAl61+/P4t+yrxkxOy1vLk16sYPXcDQzvV495LT/c7pOBVqAM3ToUfn4ZZz7sPR3LRc7B3mxr0blODPQeP0P6J7zhwOOt1gyJSuGlqqIiIFF6HE2HOa/BKa5h4EyTthW7PwT0r3ShfVkmgSBBemP47b8/6k+s71uHf3c7AFLZCQuGR0OX/YPBkOHIA3rkY5r3pRs2DVLFUCc5rVJkv47aSnl40qsyLyFFKBEVEpPDZuwG++Te80Ay+/Q+UrwP9P4Y7Frk+gEFOgxPJyqvf/8GrP6yh/1m1eKRHs8KXBAaqdz4M/xkadIFvHoCx/VyF3CD1bBXL5oQkFm/cG8IgRcQPmhoqIiKFx6YFbv3fqslgwqDZldDxdoht43dkUkSM/Gktz0//nT5tavDElS0ICyvESWCGUpVgwMcwf6TrOfjmudBnJNS/IMdDL2lajaiIMCYv20K7uhXzIVgRyS8aERQRKYzixrvG6I+Ud9/jxvsdUeikp8GKz+EtoYiZAAAgAElEQVSdS+Ddi13xi3P+Bn9fBle9oyRQ8szoOet58uvfuKJFdf53dUvCi0ISmMEYOPsWGPqD13OwV1A9B8tER3JRk6p8vXwrqWnp+RSsiOQHjQiKiBQ2ceNhyp2Q4jV4TtzkbkPRKoCSvB+WfOjWNSVsgAp14fL/QetrIaq039FJEfPx/I08PHkFlzStxkv9WxMRXkQ/K8/oOfjNA67n4PpZ7gOVCnWzPaRnq1im/rqNuet206lRlfyKVERCLMdE0BgTbq1Ny49gREQKjLjx/rQXSE+HlENw5KAr8HDkYMCXd3vav48mgRlSkly8RSERTNgE80fAotGQvA9qdYBLH4cmV0BYeM7Hi+TSp4vi+fdny7ng9Cq8NrANkUU1CcyQ0XOw/oUw5S7Xc7DHS9D8qix379ykKqWjIpi8dIsSQZEiJJgRwTXGmInAe9balaEOSKTQ8CtRkNALdsQtLTVTspbdz7m4L+XgyceduAnevRSqNIYqTY5+L1vDTQsr6DYvcu0fVnzubjft5db/1Wznb1xSpE1ZtoX7Jy6jY/1KjBh0JlERxejDhuZXeT0Hb3ZVd9f+4EbdMxVbio4M59Jm1fhmxTYev7J58XqMRIowY3MoI2yMKQP0B27ErSkcBYyz1u4LfXjBa9eunV24cKHfYUhxkTlRAIiMgR6vKBksCl5s7pKqzMIioEzs0cQtLTn4c4aXcG+uSpT2vpfK4naQ9713Oezbcvw1SpSC6m1g529wKKAqYInSxyeHVRpDudoQ5vPIR3oarJ7qCsBsnAtRZaHt9W4tU/na/sYmRd60Fdu47aPFtK1dntE3tadkiWK6YiYtBX58Cma9AJUaQqv+sOj9Yz7onBF1ITe+t4CRg87k0man+R2xiGTDGLPIWhvUJ6g5JoKZTnw+8DFQHpgI/Ndau+akosxjSgQlX2WXKJSrBXf/mv/xSN5IS4HfvoIJg7Pfp9WA3CdxkaUgokTexRnMBxEHd8HO1S4pDPx+YNvRYyJioMrpmRLEJm6tUKinYCYfgKVjYd4bsPdPl5R2uBXaXOcKWYiE2IzfdjBszEKaxZZjzJD2lImO9Dsk/62bCZ9c56ZkB4qMIfWKlzlrcgXObViZ1wa29Sc+EclRbhLBoNYIAlfgRgTrAs8DHwGdgK+B0086UpHCKjE+d9ulYDuww61HWzgK9m8BEw5ZLY0uVwuufCv/48ssI9k70dTkUpXdV91zjz02aS/s/D0gQVwF62dD3CdH9wmPgsqNjh9FrFjfNak+FYmbXQn7Re+5ZvA12sHFD0OTHhBeTEdjJN/9vGYXt3y4iManlWH0TUoC/1L/AleIKXMimJJExIz/0q3Fx3y6OJ6DyamUitLrVaSwC+ZV/AcwA3jWWjsnYPtEb4RQpHhJ3u/eDKcdOf6+cjXzPx45Oda6NWm/jIAVn0F6CjS4CLq/4BKUL+86fsSty0P+xZtZy74nNw05pgLUPtt9BTq8D3ZlJIhekhi/AH799Og+YZFQqcHRkcOMBLFSA4iIOvZ8mdfQth3szr9iEth0OKMHdLwDarXP/e8gcgp+WbebIaMXUL9yKcbcdDblYpQEHmPf1qy3J26iZ4sqfPTLRr5btZ1erWvkb1wikueCSQRbWmsPZHWHtfbOPI5HpGA7cgjG9nNTCMNLHJ8MntHDn7gkeCmHXTIyfyRsWQIlysBZQ+Csm90oWAYTVryKAUWXdUVZMhdmOXLQSxADppdui4OVXwDe0gIT7kYLMxLDpARYOgZSvTWUiZtgxuNupPGsodBh+AlL1YuEyuKNe7np/QXUKB/DmCFnU6FUHk7ZLirK1cx66QPQ/ovO3FeqC98vjlEiKFIEBFMsZjTwd2ttgne7AvC8tfamfIgvaFojKCGXchg+7g9/zoQ+b7tRjYxEoWysGy3ZFw99RmZbglt8lLDRTf1cNBqS9riEpf1QaNkPosr4HV3hk5IEu9ccTRB3rHI/71mX9bRacNVL71HxafHH8vhEBr4zj4qlSjD+lo5UKxvtd0gFU3ZrkNsNhW3L4M+ZHLRRRJw5iKjzbncfAolIgZGnawRxI4IJGTestXuNMW1OOjqRwij1CIy/HtbNgN5vQour3fbAEaLD+2BsX1eGOzUZWg/0J1Y5ylr48yc3+rf6a7etcTdXkbJup8LRUqGgioxxjalPa3Hs9tRkeLwaf40WBsqq0qlIPli1dR+DRv1C2ehIxg7toCTwRHJYg7x66Rx+/fRJei95Hxa/6/p7drwDanfQ/6kihUwwiWCYMaaCtXYvgDGmYpDHiRQNaSkw8Ub4Yxp0fyn7BC+6LFz3KXw8AD6/1X2aetaQ/I1VnOT9sGwczH8bdq2GkpXg3Lug3U1Qvpbf0RVtEVHZTy3TGlrxwZod+7nunV+Ijgjn46EdqFE+xu+QCr4TrEE+vVVHhk2/lx/KHuL1hotgwbvw25cQ29b1/WzaW4WfRAqJYF6pzwNzvKbyANcAT4QuJJECJD0NPrvF/ZHr+gy0u/HE+5coBQPHu9HDr+5xoyMdb8ufWAV2/eGSv6Vj4ch+iG0Dvd+CZldCpEYA8k2Xh7KeWlaQiu1Ikfb5ks08O201WxKSMAZKlghnwvCO1K5U0u/QCj1jDD1bxfL6jDXsGPgPqna6F5Z9DHPfgE+HwHePuFkXba+H6HJ+hysiJ5BjJ2Fr7QfA1cB2YAfQx1o7JtSBifguPR2+uMNVTbzkMVfgIhiR0dDvQzijJ0z7F/z0XGjjLO4yGpJ/0Btea+faEjTpBjf/AMN+hNYDlATmt5Z9XU/DcrUA474H9jgUCaHPFsfzwKQ4NickYYF0Cylplrj4RL9DKzJ6tool3cLXcVvdB6Bn3Qx3LIT+H0P5OvDtg/BCU/jmX7B3vd/hikg2gm4ob4ypCvz1bspauzFUQZ0MFYuRPGWtax+w6H3o/B+44B+5P0daKnw+HJZPgE73wUUPav1EXjq0B5aMgQXvuEIwZWLhrJtcm4LSVf2OTkRCJDk1jW2Jh9mckMSWhMNsSUhiS0KSdzuJdTsPZrVClRrlY/j5gYvyPd6iqutLP1GyRDiTbjv3+Du3LHEjhGoXI5Lv8rqhfE/c9NBY3IhgHWAV0OxUgpQCIHOfr6JeHj9Y1sI3D7gksNO9cP79J3ee8Ai4cgRERMOs5yD1MFz6uJLBU7V1mSv+snyie0zrdnKPa+MrtC5FpJCz1rL3UMoxiV3Gz5u9pG/n/uTjjqtcOooa5aM5vVoZ1u48mOW5tyQkZbldTk6PVrE8O201m/YcolbFTFNuY9vAVW/DxY+4/68XvedaztQ8yyWETbrr/2uRAiCYV+F/gQ7Ad9baNsaYzsCA0IYlIZe5PHTiJncbincyaC1Mfwh+ecv9sbro/04tcQsLd1PiImNg7mvu8e72HITlOCtbAqUegVWT3RuKTb9AZEloNcC1f6imz6RE8lvgGrzY8jHcf1ljerfJua/c4RQ3mnc00fNG9BKPJn6HU9KPOSYqIowa5WOILR9D58ZViPV+ruF9nVYumujI8L/2P/fpH9icRdIXqyIxeaqnlwhOidvCbRc2zHqncjXgkkfdB6pLx8K812HCYChfG86+Fdpc5wqtiYgvgukjuNBa284YswxoY61NN8bMt9YWqPF9TQ3NpRebZ1PVrxbc/Wv+x1NQzHgSZj7j1jt0ey7vRu+she8ehp9fhtbXQs9XXZIoJ7ZvqxuZXfQeHNju+lWdNdRVbo0p73d0IsXS50s2869Jy0lKOdovMiYynCevbE6n06sEjOIdnbaZcXvXgeNH86qUifISu2hiy8Uck+jFlo+mYqkSmFz8X5xdfE/1aRFUsirB6/36zySnpjP1752COyA9zbXymfs6bJwLUWVdUZmzh6uis0geyes+ggnGmNLAT8BHxpgdQOqpBCgFQGJ87rYXBz8955LANoPg8mfzdgqnMXDxo24k68en3Mhgn5EQHpl31ygqrIWN89zo36rJ7o1Do0uh/TBocJFGU0V89uy01cckWQBJKWncPX7ZcftGRx4dzTujetm/krzY8tF/jeZFReTth2IZyd7JjFhK7vRsFctjX65kzY79NKxaJucDwsLdesEzekD8IjdCOO9N99Wst2s/UePM0AcuIkBwI4KlgCRchdFrgXLAR9ba3aEPL3gaEcyFfVvgpRaQnkU+X64m3L0i/2Py25zX4Nv/QMt+rmF8KEfrZr/kRgebdIerR7m+a8VR5jWqFzwANs21f9i+3JUdbzPI9f6r1MDvaEXEU++Br7IsxgLwcI+mx0zbLF8yMlejeVK47Nh3mLOf+p6/dW7IPZc2PrmTJGyC+SNg0WhI3ge1O7qEsHE3zZwROQm5GRE8YSJojAkHpllrL86r4EJFiWCQdq2BMVe6aXYAaZmm6VSoB9d/DhXq5ntovpn/Nnx9n2uCe9W7+bOA/ZeRMPV+aHgx9B0DJYpZb6vMa1QDVW0GZw+DFte4suQiUqCc89T3bEk8fNx2VeUsngaMnMe2fYf54d4LTi3pT94Pi8fAL2+6StAV6kKH29xyiqjSeRavSFGXm0TwhHOsrLVpwCFjjDqCFgVblsCoSyHlEAyZBr1eO7bPV/uhcGg3vNUJfp3kd7T5Y/EHLgls3A2ueif/qpidPcwVkVnzPYztC8kH8ue6BcX3j2WdBJaqCrf+DGfeoCRQpIBqUv34KYAxkeHcf9lJjghJodazdSx/7jrIr5v3ndqJospAx9vgb0vgmtHu78HUf8CLTV0Rt8TNeROwiPwlmKmh43FVQ6cDf9VkttbeGdrQckcjgjlYNxPGDYSYijDoM6icTYWvvevh05shfoFbwN316aL7hnzZJ/DZLdCwC/Qf688Uzbjx8NlwtybiuoluOmRRd2AnPJfN8w8DjyTkazgiErw5a3dx7Tu/0KFeRTbuSdIaPCHh0BHOeuI7bjinLv+5omnennzTfFdYZtVkMGHQrI+bNrrrd7W/EslGnk0N9U42OKvt1trRJxFbyCgRPIEVn8OkoVCpIVw3CcpWP/H+aSmuoMmsF6ByI7eO7bQW+RNrflnxGUy8CeqcC9dOcO0d/LLyCxdLteYuSS9Z0b9YQinlsGvLMet5tw4kK8W9aq1IAZZw6AhdX5pFyRLhfHnneZQsoT5w4gx5fwErt+7j539eRFhYCNaE7l3vllQs/gCO7HdJoQ1oMxIZ42bZKBkUybupoeASvqy+Tj1MyRcLR8GEGyC2Ldz4dc5JILhKll0ecmsFD++Dty+CX0a4ao5FwW9fuVHPWmfDwE/8TQIBmvZyI5I7VsH73eHADn/jyWvWuqnGr5/liuTUORcueez4xz0yxj3vRKTAsdby78+Ws+tAMi/3b6MkUI7Ro1UsWxMPs3DD3tBcoEJd6Pok3LPCzZyxx/aaJCXJjRCKSK7kmAgaY/40xqzL/JUfwckpsBZmPgtf3u1K7w/6DGIq5O4c9S9067Xqd3bz9D8eAAcLVLHY3PtjOowfDNVbwcDxBWfa6+mXuaR0zzp4r5ur7FoUxC+Edy+FiTe6flGDPoeB4+Dcv7tPbwPXqOrTXJECa+KieL5evo17L21Mi5rFYAq75MolTasRHRnG5GUhXscXXc59QJ2V4tz+SuQkBTM1tFLAzWjgGqCitbZAfXSvqaEB0tPhmwdcOeaW/V1RmFPpV2etGxGc/n9QspLrf1fv/LyLN7+s+xHG9oPKp8PgyblPjPPDhjnwUV8oVQmunwwV6vgd0clJ2ATfPwrLJ7gF/13+z1V+UylwkUJn/a6DXPHKLJrXKMfYoR0ID8XUPyn0bh+7mLlrdzP/312ICA9hv9cXm0PipuO3h0fBHQsK799NkTyS11NDdwd8bbbWvgSoPnRBlXrErQecPwI63uF64p1q03JjoMNwuPl7KFEaRveE7//r1hIWFhvmuBHNivXdqFRBTAIB6pwD138BSXvdyODutX5HlDvJ+930nNfawaop0Ok+uHOxKzykJFCk0ElJS+euT5YSHmZ4sV9rJYGSrR4tY9lz8Ag/rw3xzKEuDx2/tCC8hPv+1nmuEFxRWcoiEmLBTA1tG/DVzhgzHDi+dnTWx3Y1xqw2xqwxxjyQxf33GGNWGmPijDHfG2PqBNyXZoxZ6n1NztVvVVwdOQgf94dfJ8LFj8Clj0NYHn4qV70l3DIT2lwLs55zicreDXl3/lDZtAA+ugbK1nBJVqlKOR/jp5pnwuAvITUJ3rscdvzmd0Q5S0+DRe/DK21dMZgzesIdC91IYFRQ/12ISAH06vd/sHRTAk/2aUFseZ/XU0uBdmHjKpSJimDy0hAvbWjZ9/ilBb1ehzvmQ9Wm8NkwV4AtKUTrFUWKkGCmhs4IuJkK/Ak8b61dncNx4cDvwCVAPLAAGGCtXRmwT2fgF2vtIWPMrcCF1tp+3n0HrLVBdxAt9lNDD+1xyc6WxdDjZTcCE0rLJ7r1hxjo8RI07xPa652sLUvdCGbJil6xnFi/Iwrejt/gg56QnupGMau39DuirK2dAdP+AztWQK0OcNmTLpkVkUJt4fo99B0xlyvb1OT5vq38DkcKgfsmLGPar9tY8ODFREf6MAskPQ1mv+gqn5eu5mZF1b8g/+MQ8VFeTw3tHPB1ibV2WE5JoKc9sMZau85aewQYB/TKdO4Z1tpD3s15QM1ggpZMEuNhVFfYthz6jgl9EgjQ4moYPguqnO4KgUz+mxuRLEi2r4AxV7rF5YOnFK4kEKBqE7hxKkTEwOjuEL/I74iOtXO1W884pjccOeAaAN/0jZJAkSJg3+EU7vpkKTUrlOSRnnncG06KrB6tYtmfnMqPq3f6E0BYOJx/HwyZ7qaPftALvn0QUpP9iUekgAtmauiTxpjyAbcrGGMeD+LcNYDA1bzx3rbsDAGmBtyONsYsNMbMM8b0zia2Yd4+C3fu9Ok/Hb/t/B3evQz2b4VBk+CM7vl37Qp1XaLS6V5YPAZGXuiS0YJg5+/uD0BENAz+AsrX8juik1OpgRvJjKngfp8Nc/yOyFWO/eo+eKMjbJzrWkHcPh+a9XbrSUWk0Hv4ixVsTTzMi/1aUyb6FNeZS7FxboNKVCpVginLfK58XaMt3PITtLsR5rzq2mDtWOVvTCIFUDALyC631iZk3LDW7gW6BXFcVu8Is5yHaoy5DmgHPBuwubY3rDkQeMkY0+C4k1k70lrbzlrbrkqVKkGEVMTEL4JRl0HaEbjhK6h7Xv7HcFzPwS6u6aufC7V3r4XRPQDjqoNWrO9fLHmhQh2XcJc5DT68yk3F9ENqsvuD+kob15+y3Y1w5xLXCiIy2p+YRCTPfbF0M58t2czfLmrImXUKaGEtKZAiwsPo1qI6363azoHkVH+DKVEKur8IAz6B/dtgxAUw7y1XWV1EgOASwXBjTFTGDWNMDBB1gv0zxAOBwzA1geM+IjLGXAz8B+hprf1r7N5au8X7vg74EWgTxDWLjzXfu2QnuiwMmeb/+rH6F3o9By+Eqff713MwYaMbOUtPcUlg5Ub5H0MolI11I4MV6rkWGL9Py79rWwsrv4DX27spNrXPhlvnwBXPQ6nK+ReHiITcpj2HePCzX2lbuzx3dG7odzhSCPVoFUtyajrfrdzudyhO465w21z3/uSbf8JHV8G+rX5HJVIgBJMIfgh8b4wZYoy5CZgOjA7iuAVAI2NMPWNMCaA/cEz1T2NMG2AELgncEbC9QkbyaYypDJwLrESc5RNdMlCxPtw0reCMeJWq7Jqid30G1n4Pb50Lf/6Uf9dP3OyS4+R9rrhK1TPy79r5oXRVuOFL93uNu9YlZ6G2eZGrXDr+erdW8bpJcO0Et35RRIqUtHTLveOXYYGX+rUJbS84KbLa1alA9XLRTPZ7emig0lXd+5MrXoANc+HNjrBSBelFgikW8z/gceAMoBnwX29bTselAncA04BVwHhr7QpjzGPGmJ7ebs8CpYEJmdpEnAEsNMYsA2YATwdWGy3W5r8Nn94Mtdq7pKDMaX5HdKyseg7+8DikhXiKyP7trsLmwd0w6DP/R0hDpWRFN9JZoy1MuBHixofmOonxMGmYW1exew10fwmGz4aGXUJzPRHx3Vsz1zJ//R4e69WM2pVK+h2OFFJhYYYerWL56fed7D14xO9wjjIGzhriCt2VrwPjB8EXt7v+tyLFVDDtI+oBW621h73bMUA1a+360IcXvCLfPsJaVw555jPQuBtcPer4hqoFzZGDMPUfsORDqNkernrHrXfLawd3wftXQMImVzCndoe8v0ZBk3zA9YxcP9u1CzlzcN6d9+eX3VpAmw4db4fz7nZTkEWkyFq6KYGr35zD5S2q80r/1hgVfpJT8OvmRLq/Opun+rRgQPvafodzvNQj7v3U7BegfG3o87b7gF2kCMjT9hHABCBwZW2at03yS3oafHWv+0+r9XWuRURBTwLBLdTu9Tpc9S7s/A3e6gQrPsvbaxza49oX7F0PA8cVjyQQIKq0m6LZsAtMuRN+GXFq50tPc5VfX20LP/0PmnSDOxbAxQ8rCRQp4g4mp3LXuCVULRPF472bKwmUU9Ystiz1KpcKfXP5kxVRArr8H9zwtfvQc9RlMONJSEvxOzKRfBVMIhjh9QEEwPu5ROhCkmOkJsPEm2Dhu3DuXdDrNQiP8Duq3MnoOVi5EUy4Ie96Dh5OdFU0d66G/h9BvfNP/ZyFSWQM9B8LTbq7kdfZL53cedbNdNXUJt/hPhkdMt2NOIdi9FZECpzHpqxkw55DvNCvNeVi1CpCTp0xbnrovD93s33fYb/DyV6djm7ZQ8t+7sP2UV1d5XGRYiKYRHBnwJo+jDG9gF2hC0n+krwfxvaFlZ/DpY/DJY8W3j5tFeq6ZuPn3ZM3PQeTD8BH18C2OOj7ATS8OK8iLVwiouCa96H5VfDdwzDjqeBbd+z6w1V3/aCnS6qvHuWSQE2PESk2pi7fyicLN3HrBQ3oUL+S3+FIEdKzVXWsha/iCniFzuhycOVbcPV7bk38W51g0Wh/22CJ5JNg1gg2AD4CYnG9ATcB11tr14Q+vOAVuTWCB3fBR1fD1jg3vbL1AL8jyjvrfoRJt0DSXpfgth+auwT3yCGXIG+YA9e8B017hSzUQiM9zY20Lv3I9fW7+AQfGhza4z75XPCOqwTa6R7ocJt6AYoUM9sSD3PZSz9Rp1JJJg4/hxIRqhIqeevyl2cRFRHG57ef63cowUncDJ/fCn/OhMZXQM9X1CZJCp08XSNorV1rre0ANAWaWmvPKWhJYJGTsNHNV9+xyk39K0pJIAT0HLzA9RwcN9AlJ8FIOQyfXOuKpFw5QklghrBw6PkatBviir1M/cfxTXNTj8DcN1xD+Pkjoc0guHOxSwSVBIoUK+nplnsnLOVIajov9WutJFBComerWJZuSmDj7kN+hxKccjVc+6lLn4A10+GNjvDHdL+jEgmZoBabGWOuwLWOiM5YRG6tfSyEcRVfO1bBmCsh5ZD7z6hOR78jCo1SlWHgePjlLZj+ELx5LvQZCfU6ZX9M6hGYMBjW/uBGSVtek3/xFgZhYa7Je2QMzH0Ntq+EhPXuE86SldwI4cGd0OAi90euWlO/IxYRn7w7+09+XrObp/u0oH6V0n6HI0VU95bVeeab35gSt4XbOzf0O5zghIXBOXe4D60nDXWzs9oPg0seKxyF+kRyIcePAI0xbwH9gL/hpoZeA6iKRChsmu8WKlsLN04tuklgBmOgw61w83dQoqRrBp9dz8G0VPj0Jvj9G9cQts11+R9vYWCMm27b+ArYMNv1A8TCoV1uuvE5d7qm8EoCRYqtFVsS+d+037i0aTX6nVXL73CkCKtVsSRta5dnSkFqLh+s05rD0Blu6cT8ka6o2tZlfkclkqeCmQtyjrX2emCvtfZRoCOgvxx57Y/prvl6yYowZBpUa+Z3RPmneisYNhPaXAs/PQvvd3PTY+PGw4vN4ZHy8HQtWDUFLnvKNYSV7Bnjiugcx7r2HYW14JCInLKkI2n8fdxSKpQswdNXtVSrCAm5nq1i+W3bfn7fXggbt0dGQ9enYNBnkLwP3u4Cs1906/JFioBgEsEk7/shY0wskALUC11IxVDceNccvMrpcNO3rsJmcRNV+mjPwR2r4NX28MXtkLgJsG6qbFikFm0HKzE+d9tFpFh48utVrNlxgOf7tqJiKXWCktC7omUsYYaC21MwGA0uglvnQOPL4btH3Af3CZv8jkrklAWTCH5pjCkPPAssBtYDH4cyqGJl3ptuDnrtjjD4Syhdxe+I/NXiarjlJyAN0o4ce196CnyvpalBKVczd9tFpMj7ftV2xszbwM3n1aNTo2L+t0byTZUyUZzToDJT4raQU6X6Aq1kRdeuqtcbsHWpq20QN8HvqEROSTBVQ/9rrU2w1n6KWxvYxFr7UOhDK+KsdUnNNw/AGT3g2okQXdbvqAqGivUgLSXr+zSiFZwuDx2/qD0yxm0XkWJn5/5k/jExjianleH+ro39DkeKmR6tqrNh9yHi4hP9DuXUGOOWsQyfDVWbwKSbYeIQSErwOzKRk5KretHW2mRrbSF/FRcA6Wkw5e8w63k48wa4ZrTK92emEa1T07Iv9HgFytUCjPve4xW3XUSKFWst909cxoHkVF4Z0IaoiHC/Q5Jipmuz6kSGGyYXxqIxWalYD274Gjo/6Nbev3ku/DnL76hEck2Ng/JbymHXAmHxaDj/fuj+kusBJ8fSiNapa9kX7v4VHklw35UEihRLH8zdwI+rd/LvbmdwerUyfocjxVC5kpFccHpVvozbQnp6IZ4eGig8Ai64H4ZMh4goV/l8+kOu1ZVIIaFEMD8d3uf60ayaAl2fgYseVAXH7GhES0TklP2+fT9PfL2Kzo2rcH1HdX4S//RoVZ3t+5KZv36P36HkrZpnutoGZw6Gn1+Gdy6C2S8drXr+YnNXFFCkAMq2obwxpu2JDrTWLs77cIqwAzvgw6tgx5CbXawAACAASURBVEro846aoQejZV8lfiIiJyk5NY07P15CmagI/nd1K7WKEF9d0rQaMZHhTF62hQ71K/kdTt6KKg09XoZGl8GnN8N3Dx+9L3ETTLnT/az3NFLAZJsIAs+f4D4LXJTHsRQtceNdMZjEeChzGqSnwpGDMOATaHSx39GJiEgR9+w3q/lt235G3dCOKmWi/A5HirmSJSK4uGk1pi7fyqM9mxEZXgQnpTXpBtHlIOXgsdtTkty00RbXaCaYFCjZJoLW2s75GUiREjfeffqT4rVg3L/Vfb/gX0oCRUQk5Gb9sZN3Zv/J9R3rcFGTan6HIwJAj5bVmbJsC7PX7KJz46p+hxMaGe/5str+dB04rcWxX1WaQIR6eoo/TjQi+BdjTHOgKfBXaUtr7QehCqrQ+/6xo0lgoKUfQucH8j8eEREpNvYcPMK945fRsGpp/t3tDL/DEfnLBY2rUDY6gilLtxTdRLBcTTcdNLOYCtCsD2xb7goGphxy28MiXTKYkRhWbwnVmkNM+fyNW4qlHBNBY8zDwIW4RPBr4HJgNqBEMDvZ9bpTDzwREQkhay3//DSO/2fvvuOzKu//j78+CQkJKwHCCissGQIBBQcgoiBDJWKlasVdv9b2p1atWq3WWlsrFW1dbd2jdYCiRXCBgCBYByABlCF7JBCWQAIJWdfvj3MCSUjIHTLuO8n7+XjkkdxnXOdzzn0F7k+u9eOhbF65biBREZqVWkJH/XrhjO7dmg+XbycrJ6921s/hDxTtFQberOdjHj06RjA/D/ZuhB3LvcRwx3JYPweWvXn0nNgO0Lpv0dbDmPbqWiqVKpAWwfFAIrDUOXedmbUCXqzasGq40v4apDXwRESkCk1etJVPV6Zx3/k9OTk+JtjhiBwjKbEtby/exmerdzKmT5tgh1P5CpK9gnkiYtp5yWHhiWLCwiGuq/fV+ydHt6enQdoKPzn0v1Z/iDc1B974w+LJYVx3dS2VExZIIpjpnMs3s1wzawLsBDpXcVw1W2l/DdIaeCIiUkU27MrgoRkrGdy1OT8f0inY4YiU6IzOzYhrFMn0Zam1MxGEE5/1vHEr76trofkksg/CzlWFWg9XwJJXi3YtbdmjUILYF1r39pJGkTIEkgguNrNY4AVgCZABfFOlUdV0gfw1SEREpJJk5+bz68nJ1I8I4/Gf9iMsTN3HJDTVCw/jgj5teGvRVtKzcmgcFRHskEJbZENoN8D7KpCfB3s3eMnhdj9BXDsLkt84ekxsx0KJYUHX0nZe19LCM9vrM2qdZs65wA82SwCaOOeWV1VAJ2rAgAFu8eLFwQ5DRESk2j36yWr+OW89z155KqN7tw52OCLHtWTzXi7515f87dJEfnKKhs1UmvS0o2MOC1oP96zjaNfSWGjUCvau95Y1KxARDWOfUjJYS5jZEufcgLKPDGyymPeBKcD7zrlNFYxNREREKtFXG/bwr/nruXxgeyWBUiP0b9+UtrHRTF+WqkSwMhV0Le1WrGtp2sqjyWHyG0WTQPCGMs15SIlgHRTIap5/A4YAK83sHTMbb2ZRZZ0kIiIiVWv/oRzumJJMx2YN+P2FvYIdjkhAwsKMCxPbsHDtbvYezA52OLVbZENoPxAG/hzGPgF5OSUfp5nt66QyE0Hn3Hzn3K/wJoh5HrgUb8IYERERCRLnHL+btoKd6Yd58vL+NKwf0NLAIiEhKTGe3HzHx9+VsgC7VI3SZrBv0rZ645CQEEiLIGYWDVwC3AQMBF6ryqBERETk+N77NoUPl2/n9vNOIrG9Fp+WmqVXmyZ0btGQ6cmpwQ6lbhn+gDcmsLgm8VCOeUOkdigzETSzKcAq4FzgH0AX59wtVR2YiIiIlGzLnkP8Yfr3nJbQjJvO7hLscETKzcxISoznm0172bE/K9jh1B19L/UmholpD5j3vdc42PYNfPlMsKOTahZIP5JXgCucc3lVHYyIiIgcX25ePrdNWYoZ/O2yRMK1VITUUEmJ8Twxey0fLE/lhrO0RHW1Kb7OoXPw9tXw6QPQ6mTocm7wYpNqFcgYwU+UBIqIiISGZz5bx7db9vHncb1p17RBsMMROWGdWzSid9smzFim7qFBZQbj/gUtesDU62HvxmBHJNUkoDGCIiIiEnxLNu/lqTlrubh/Wy7qp8kdpOYb2zeeZdv2s2n3wWCHUrfVbwSXvwEuHyZP8JadkFpPiaCIiEgNkJ6Vw21TkomPjeaPF50c7HBEKsWFifEAahUMBc06w/iXYdcqmPYrTR5TBwQyWcycQLaJiIhI1fnD9O9J+TGTJy7rR5OoiGCHI1Ip2sZGMzChKTOWKxEMCV1HwIgHYeU0WPj3YEcjVazURNDMosysGRBnZk3NrJn/lQDEV1eAIiIidd2MZam8920KN5/bjQEJzYIdjkilGpsYzw9pGazecSDYoQjAoFuh9yUw5yFY+2mwo5EqdLwWwV8AS4Ae/veCr/fxlpEQERGRKpayL5P7/ruC/h1iufXcrsEOR6TSnd+nDeFhpjUFQ4UZJD0DrXvD1J/DnvXBjkiqSKmJoHPuSedcJ+BO51xn51wn/yvROaeFRkRERKpYXr7jjinJ5OU7nrisH/XCNbRfap+4RvUZ1KU5M5an4jQuLTRENoDL3oCwcJh8BRxOD3ZEUgUC+R9lh5k1BjCz+83sPTM7pYrjEhERqbOmLU1h8MS5dPndR3y9cS9J/eLp2LxhsMMSqTJjE+PZujeT5K37gh2KFGjaEX76CuxeC/+9CfLzgx2RVLJAEsHfO+fSzWwIMAp4DfhX1YYlIiJSN01bmsI97y0nZV9mkW3TlqYEMSqRqjXq5NZEhocxXbOHhpbOw2Dkn2H1B7DgsWBHI5WsXgDHFCwmfwHwL+fc+2b2YCCFm9lo4EkgHHjROTex2P47gBuAXGAXcL1zbrO/7xrgfv/QPzvnXgvkmiIiIqHMOceBrFxS92Ue+UrZl3Xk56VbfiSvWO+4zJx8Js1cw7j+WjtQaqeY6AiGdW/Bh8u3c/8FvQgPs2CHJAXO+CVsXwafPQytekOP84MdkVSSQBLBFDN7DhgB/NXM6hPYshPheJPKnAdsAxaZ2XTn3MpChy0FBjjnDpnZL4FHgcv82Ur/AAwAHLDEP/fH8tyciIhIdcvJyyftQBapfnKX4n8dTfyyyDicW+SciHCjTUw08bFRxySBBVILtRCK1EZJ/eKZtTKNrzfuYVCXuGCHIwXMYOwTsGs1vHcj/N9caHFSsKOSShBIIngpMBp4zDm3z8zaAHcFcN5pwDrn3AYAM5sMXAQcSQSdc58VOv4r4Er/51HAp865vf65n/oxvBXAdUVERI5r2tIUJs1cQ+q+TOJjo7lrVPeAWtuccxzIzD2a2O0vSPKOtuilHcgiv1gy16xhJPGxUSQ0b8igLnG0jY0mPtZL/NrGRhPXqD5hfgvI4Ilzi3QLLRAfG10p9y4Sqob3aEWDyHBmLEtVIhhqIqLh8jfgubNh8s+8ZDAqJthRSQWVmQj6rXU7gSHAWrxunGsDKLstsLXQ623A6cc5/ufAx8c595j/oc3sRuBGgA4dOgQQkoiI1HXTlqZw73sryMzxRj6k7Mvk3vdWAN409mkHsoq04BXutpm6L5OD2XlFyosMDyM+Nor42GgGd40jPjaatv7r+Nho4mOiiY4MDzi+u0Z1LxIfQHREOHeN6l4Jdy8SuqIjwzmvVys+WrGDPyb1JrKeZskNKTHt4NJ/w7+TvJbBy9+CML1HNVmZiaCZFXTR7A68AkQArwODyzq1hG0ldngxsyv9a5xdnnOdc88DzwMMGDBA8w2LiISIE21xqwzOObLz8jl0OI+D2bkcys7j4OGj3/844/siSRZAZk4ed7ydzO1vJ1N89vrmDSOJj42mc4uGDOnmteYdbdGLpnnDyCOteZWh4DkF6/mJBFNSYjzvJ6eycN0uzu3RKtjhSHEJg2H0RPjoTpj3CJx7X7AjkgoIpGvoxUB/4FsA51xqwXISZdgGtC/0uh1wzFRQZjYCuA842zl3uNC5w4qdOy+Aa4qISJAdr8WteDLjnCMzJ4+Dh/M4lJ179Ht2HocO+98Lbz9m/7GJ3qHsPHKL980MQL6DXw/vVqTbZnxsNFERgbfmVZZx/dsq8ZM66axuLYiJjmB6cqoSwVA18AbYngyfPwqt+0CvpGBHJCcokEQw2znnzMwBmFmgCxktArqZWScgBbgcuKLwAWbWH3gOGO2c21lo10zgL2bW1H89Erg3wOuKiEgQTZq5psQWt7unLueFBRs4ePhoIncoJ++YFrjShBk0jKxHg/rhR743iKxH84aRtG/WgIaR3uuG/vaGkeE0qF+v6DmR4fz8tUWkHTh8TPltY6O5/TxNgCASTJH1whjTuzXTl6WSmZ1Xrm7VUk3M4PzHYedqb33B5l2hVa9gRyUnIJBE8G1/1tBYM/s/4HrghbJOcs7lmtnNeEldOPCyc+57M3sIWOycmw5MAhoB75gZwBbnXJJzbq+Z/QkvmQR4qGDiGBERCW2lzW6ZnZdPm5joEhK1Qt8LJ3KFkreG9etRv14Y/v8VFXLvmJ4agycSwpIS45m8aCtzV+/kgr5tgh2OlCQiCi77Dzw/DCZfATd+BtFNyzxNQou5AP4Ua2bn4bXKGTDTOfdpVQdWXgMGDHCLFy8OdhgiInVeabNeto2N5ot7zg1CRMcK5hhGETm+vHzHGY/M4ZQOsTx31YBghyPHs+VrePUC6DQUJrwDYWrBDTYzW+KcC+gXJ5AWQfzE71MziwP2VCQ4ERGp3S5MbMNz8zcU2RZqLW4agycSusLDjAv6tOHNb7ZwICuHJlERwQ5JStPhdLjgMZjxa5jzEJz3x2BHJOVQ6pyvZnaGmc0zs/fMrL+ZfQd8B6SZ2ejqC1FERGqKvHzH/DW7aNYwgviYKAyvJfCRn/RR4iUiAUvqF092bj6zvk8LdihSllOvhVOvgy+egO/eDXY0Ug7HaxF8BvgdEAPMBcY4574ysx54C7t/Ug3xiYhIDfLukm2s3pHOP644RWN7ROSE9W8fS7um0Uxflsr4U9sFOxwpy5hHYecqeP9miDvJm01UQt7xVoGs55yb5Zx7B9jhnPsKwDm3unpCExGRmuRQdi6PzVpD/w6xnN+ndbDDEZEazMwYmxjPF+t2syfj2Fl+JcTUi/QWm4+K8SaPOaQ5HmuC4yWC+YV+Lj7qX4u3i4hIES8u2MjO9MPcd37PSpndU0TqtqTEePLyHR+t2B7sUCQQjVvBZW9A+g5451rIyw12RFKG4yWCiWZ2wMzSgb7+zwWv1d4rIiJH7EzP4tn56xnTuzUDEpoFOxwRqQV6tG5Mt5aNmLFMiWCN0e5UuPDvsHE+zP5DsKORMpSaCDrnwp1zTZxzjZ1z9fyfC15r+iYRETni75+uJTs3n7tH9wh2KCJSSxR0D/1m095S1yeVENT/SjjtF/DlM7BsSrCjkeM4XougiIhImdampTNl0RauPKMjneIaBjscEalFkhLjAfhgeWqQI5FyGfUwdBwCM26F1ORgRyOlUCIoIiIV8sjHq2lYvx63Du8W7FBEpJZJiGtI33Yx6h5a04RHwE9fhQZxMHkCZOwKdkRSAiWCIiJywv63bjdzV+/k/53TlWYNI4MdjojUQmP7xrMiZT8bdx8MdihSHo1awOWvw6Hd/uQxOcGOSIpRIigiIickP9/x8EeraBsbzbWDEoIdjojUUhcmtsEMpiere2iNE98fxj4FmxfCzPuCHY0Uo0RQREROyLTkFL5PPcDdo7sTFREe7HBEpJZqExPNwIRmTF+WgnNawazGSbwMzrwZvnkOlr4e7GikECWCIiJSblk5eUyauYY+bWMY2zc+2OGISC2XlBjP+l0HWbU9PdihyIkY8UfodDZ8cDtsWxzsaMSnRFBERMrtpYUb2b4/i9+d35OwMC0eLyJVa0zv1oSHGdOXqXtojRRez5s8pnFrmHIlpKcFOyJBiaCIiJTTnozD/Gveekb0bMWZXZoHOxwRqQOaN6rPkK5xzFiWqu6hNVWDZnD5m5C1H96+GnKzgx1RnadEUEREyuXJOWvJzMnjnjFaPF5Eqk9SYjwp+zL5dsu+YIciJ6p1H7joH7D1K/jkt8GOps5TIigiIgFbvyuDN77ews9Oa0/Xlo2CHY6I1CEjT25FZL0wZqh7aM3W+ycw+DZY/DIsfiXY0dRpSgRFRCRgf/14NdER4dw24qRghyIidUzjqAjO7d6SD5ZvJzcvP9jhSEUMfwC6DIeP7oItXwc7mjpLiaCIiATk6w17mLUyjZvO7kxco/rBDkdE6qCkfvHszjjM1xv3BjsUqYiwcBj/EsS0g7evggNq5Q0GJYIiIlKm/HzHXz5aResmUfx8SOdghyMiddS5PVrSMDJci8vXBtFN4WdvweEMbybRnKxgR1TnKBEUEZEyfbBiO8u27efOUd2JjtTi8SISHFER4Yw8uTUff7edw7l5wQ5HKqplT7j4WUhZAh/9BjQjbLVSIigiIseVlZPHXz9eTa82Tbi4f9tghyMidVyLRpEcyMql+/2fMHjiXKYtTQl2SFIRvZJg6F2w9HVY9GKwo6lTlAiKiMhx/fvLTaTsy+R35/ckXIvHi0gQTVuawr+/2nzkdcq+TO59b4WSwZpu2O/gpNHwyT2w6YtgR1NnKBEUEZFS/Xgwm2fmrmNY9xYM6RYX7HBEpI6bNHMNWTlFZwzNzMnjkY9XBSkiqRRhYfCT56FpJ3jjUni8BzwYC3/vDcvfDnZ0tZYSQRERKdXTc9eRcTiXe8f0DHYoIiKk7ssscXvagcNc9tyXvPn1FvYdyq7mqKRSRMXAKVdBTgakbwcc7N8KM25VMlhFlAiKiEiJNu85yH++2sSlA9rTvXXjYIcjIkJ8bHSJ2xtH1WN3xmF+998VDHx4Nje8tojpy1I5lJ1bzRFKhXzzwrHbcjJhzkPVH0sdUC/YAYiISGh69JM11AsL447ztHi8iISGu0Z15973VpCZc3TG0OiIcP50UW8u6hfP96kHmL4slenJqcxetZMGkeGM7NWKi/q1ZUi3OCLC1QYS0vZvK317fp63/qBUGiWCIiJyjCWbf+TDFdu5bUQ3WjaJCnY4IiIAjPNnLp40cw2p+zKJj43mrlHdj2zv3TaG3m1juGd0D77ZtJf3k1P5aMV2piWn0rRBBBf0bcO4fm05pUNTwjT5VeiJaed1Bz2Ggyf6QOLlkHgFxHWt9tBqI3O1ZL2OAQMGuMWLFwc7DBGRGs85xyX/+h9bf8xk3p3DaFhffzMUkZorOzefz3/YxfvLUvl05Q6ycvJpGxtNUr94LuoXT4/WTYIdohRY/rY3JjCn0FjQetHe2MEfN8G62eDyof0Z0O8KOHmcN7ZQjjCzJc65AQEdq0RQREQK+2jFdn71xrdM/EkfLj+tQ7DDERGpNAcP5/LpyjSmJaewYO1u8vId3Vs1JqlfPEmJ8bRv1iDYIcryt70xgfu3eS2Ewx+Avpd6+w5shxVvw9I3YPcaL0nsOdZLCjud7c0+WscpERQRkROSnZvPeX+fT1S9cD769VlaN1BEaq09GYf5aMV23k9OZfHmHwE4tWNTLuoXz/l92hDXqH6QI5RSOQcp30LyG/DdVMjaD03aeV1H+10BzbsEO8KgUSIoIiIn5OWFG3nog5W8ct1AzuneMtjhiIhUi617DzFjuTfJzOod6YSHGUO6xjGufzzn9WpNI3WRD105WbDmI0h+E9bP8bqOdjjTSwh7jYOoutX1V4mgiIiU2/7MHM6e9Bm942P4z89Pw0ytgSJS96zecYDpyam8n5xKyr5MoiLCGNHTm3n07JNaEFlP3Q9D1oHtsHyylxTu/sHrOtoryUsKE4bWia6jSgRFRKTcHvloFc8v2MAHtwzh5HgNvheRui0/3/Htlh95PzmVD1dsZ+/BbGKiIzi/T2uSEttyeqdmmnk0VDkHKUu8rqMr3oXD+yGm/dGuo806BzvCKqNEUEREymXr3kMMf3w+YxPjefzSxGCHIyISUnLy8lm4bjfTk1OZ+f0ODmXn0bpJFGMT23BRv7acHN9EvShCVU4WrPnQ7zo61+86OujorKP1Gwc7wkqlRFBERMrl1reWMvP7Hcy7axhtYqKDHY6ISMjKzM5j9qo03k9OZf4PO8nJc3Rp0ZCL+rUlKTGe5K37Sl3nUILsQCos87uO7lkLEQ2gZ0HX0bMC6jo6bWlKSL+/SgRFRCRgy7bu46J/fMHN53TlzlHdgx2OiEiN8ePBbD7+bgfvJ6fw9ca9AJh5PRMLREeE88hP+oRUslDnOQfbFvuzjr5XqOvoz6Dfz0rtOjptaQr3vreCzJy8I9tC7f1VIigiIgFxznHZ81+xfmcG8+4aRuOoiGCHJCJSI6Xuy2T0E59zICv3mH0tG9fn698NV/fRUJSTCasLdR3FQcfB5PS5nC2tR7Ip3di05xCb9xxkyqKtHM7NP6aItrHRfHHPudUfewnKkwhW6Vy4ZjYaeBIIB150zk0stn8o8ATQF7jcOTe10L48YIX/cotzLqkqYxURqYs+XZnGNxv38qdxvZUEiohUQHxsNOklJIEAO9MPc/akeYzo2YoRvVoyMKEZEeG1fwbLUJeZnceWvblsChvC5o792Ru5iYSUDxi05RM6bL6FNq4+yfmnMTtvKN9H9uFwbj5JYQu5u97bxNtuUl0cj+Zeyox9Q4J9KyekyhJBMwsH/gGcB2wDFpnZdOfcykKHbQGuBe4soYhM51y/qopPRKSuy8nLZ+Inq+nSoiGXD2wf7HBERGq8+NhoUvZlHrM9NjqCLi0a8vrXm3n5i43EREdwTvcWjOjViqEntaCJ/hBXZQ4ezmWz36K36cj3g2zafYgdB7KKHNu0QQQdm4/nqzZXcUbkBk4/8AnjUj/hkpwFuJj2zLd4Ts//lmjLAaCd7WZixIs0i4gELgjC3VVMVbYIngasc85tADCzycBFwJFE0Dm3yd93bBuriIhUqcnfbGHDroO8cPUA/WVaRKQS3DWqe4ljyB5MOplx/dty8HAuC9buZvaqNOau3sm05FQiwo0zOjdnRM9WDO/ZknZNGwTxDoLvRCZjOZCVw+bdh9i052CxhO8Qu9IPFzk2rlF9Epo3YHDXOBKaN6BjXEPve7OGxDQonJCfAow/0nXUkt/g7P1zKd67t4Flc3fEFOCPlXL/1anKxgia2XhgtHPuBv/1VcDpzrmbSzj2VeCDYl1Dc4FkIBeY6JybVsJ5NwI3AnTo0OHUzZs3V8WtiIjUOulZOQybNI+uLRsx+cYzNG5FRKSSBJrI5PnrFM5emcanq9LYsOsgAD3bNOG8ni0Z0asVfdrG1Kl/n0ubjOUvF/dmWPeWfqJ36Jjvew9mFymnVZP6dGzuJXgJcQ1JaN6Qjs0b0LF5QxrVr0A72IOxQEm5k8GD+0683EoUKmMES6q15ck6OzjnUs2sMzDXzFY459YXKcy554HnwZss5sRDFRGpW56dv549B7N55YKedepDhohIVRvXv21AM0iGhxkDE5oxMKEZ957fk/W7MpizKo3ZK3fyzGfreGruOlo1qe+PK2zFmZ2bExURXg13EDyPfrK6SBIIkJmTxx1vLyuSRJhBfEw0HZs3YNTJrb0WveYNSYhrQIdmDWgQWUUpTkw72L+15O01UFUmgtuAwoNO2gGpgZ7snEv1v28ws3lAf2D9cU8SEZEype7L5MUFG7moXzx928UGOxwREQG6tGhElxaNuHFoF/YezOaz1TuZvSqN/y5N4Y2vt9AgMpyh3bxxhef2aEmzhpHBDvmEHTycy/pdGaxNy2DtzgzW7Uxn7c4MUvdnlXi8A+6/oCcJfrLXrmmD4CTFwx+AGbd63UULRER722ugqkwEFwHdzKwTkAJcDlwRyIlm1hQ45Jw7bGZxwGDg0SqLVESkDnls1hoccOdIrRkoIhKKmjWM5JJT23HJqe3Iysnjqw17+HRlGrNXpfHJ9zsIMzi1Y9MjrYVdWjQKdsglOpCVw7qdGaxLy2Ctn+ytTcsoMqFORLjRKa4hvdvGsPdgdokzr7aNjeaGs0pe269a9b3U+z7nIdi/zWsJHP7A0e01TJWuI2hm5+MtDxEOvOyce9jMHgIWO+emm9lA4L9AUyAL2OGcO9nMBgHPAflAGPCEc+6l411L6wiKiJTtu5T9jH1mITcO7cy9Y3oGOxwRESkH5xzfpRzg01VpzF6ZxsrtBwDoHNeQ83p5SeEpHZoSHla9Xf73Hcpm3c4MfvATvnV+wld4Vs7IemF0adGIbi39r1aN6NqyMR2bNzgyYVlNWLA91GlBeREJeScyK5hUjHOOCS9+zartB5h31znERGu6chGRmixlXyZzVqXx6co0vtqwh5w8R9MGEZzboxXn9WrJWd1a0LAik6MUsyfjsNeqtzODdWnpR34uPDNndEQ4Xf1kr2urRnRr2ZhuLRvRvlmDgBJUfT6oGCWCIhLS9Be/4Phs9U6ue3URfxjbi+sGdwp2OCIiUonSs3KY/8MuZq/0lqY4kJVLZHgYg7p6S1OM6NmK1jFRZSZazjl2pfsJX6Fkb93OjCKzczaqX+9IwtfNT/i6tmxE29howqq5RVKOUiIoIiFt8MQ5pOw7dkB429hovrjn3CBEVPvl5uUz5skF5OTlM+v2s4msp3UDRURqq5y8fBZv+pHZfmvhlr2HAGgXG82OA1nk5h/9/B8ZHsb5fVoTFRF+JPk7UGicXuOoepzUymvV69qyEd38n9vERGnW6RAUKstHiIgcY9nWfSUmgeDNZilV450l21i7M4N/TThFSaCISC0XER7GmV2ac2aX5tx/QU/W7czg01Vp/P3TH4okgQDZeflMS06laYMIurVqzNjEeL+Vz0v4WjSur4SvllIiKCLVYtPug0yatYYPl28nzCC/hM4IYQZTl2zj4v5tq32ge2128HAuj8/6gVM7RvJK1QAAIABJREFUNmV079bBDkdERKqRmXlJXavGTPpkTcnHAEsfGFm9gUnQ6c/CIlKldqUf5vfTvmPE3+bz2eqd3Dq8G3+5uDfRxdb/iQwPIz42mjvfWcb5Ty5g7uo0akvX9WB77vMN7M44zH1aPF5EpE6Lj40u13ap3dQiKCJVIuNwLi98voEXFmwgOzefn53WgVuGd6Vl4ygAoiLqHTNY/aJ+8Xy0YgeTZq7m+lcXc3qnZtwzpgf9OzQN8t3UXGkHsnjh8w1c0KcNp+g5iojUaXeN6l7iZG13jdK6snWRJosRkUqVnZvPW99s4ak5a9lzMJsL+rThzlHd6RTXMOAycvLymbxoK0/OXsvujMOM6d2aO0d1D9kFc0PZb6cu572l25h9x9l0bB74eyAiIrWTlmeo3TRrqIhUu/x8xwcrtvPYzDVs2XuIMzo3454xPenXPvaEyzx4OJcXF2zk+c/Xk5Wbz2UD23Pb8G60bBJViZHXXqt3HGDMkwu4fnAnfn9hr2CHIyIiIlVMs4aKSLX6Yt1uJn68mhUp++nRujGvXDeQYSe1qPB4tIb16/HrEd2YcEYHnp6zlje+3sJ/v03hhrM6cePQzjSO0oLox/PIR6tpXL8et5zbNdihiIiISIhRIigiJ+y7lP389ZPVLFi7m7ax0fzt0kQu6lf5M37GNarPHy/qzfVDOvHYrB94eu46Xv9qM7ec6yWJ9euFl11IHfP5D7uY/8Mu7ju/J7ENIoMdjoiIiIQYdQ0VkXLbsucQj3+6hveTU4ltEMHN53TlyjM6EhVRPQnZim37mfjJKr5Yt4d2TaO5c2R3khLjCdOSEwDk5TsueGoBGYdzmfObs5Uoi4iI1BHqGioiVWJPxmGenruON77eTHiY8athXbhpWBeaVHMXzT7tYnjjhjNYsHYXEz9ezW1Tknnu8w3cM6YHQ7vF1fklEt77dhurd6Tz1M/6KwkUESkkJyeHbdu2kZWVFexQRCokKiqKdu3aERFx4p/BlAiKSJkOZefy0oKNPPf5Bg5l53LZwPb8evhJtI4J7qQtZ3VrweAuccxYnspjs9ZwzcvfMKhLc+4Z04O+7U58kpqaLDM7j8dmrSGxfSxj+7YJdjgiIiFl27ZtNG7cmISEhDr/R0OpuZxz7Nmzh23bttGpU6cTLkeJoIiUKicvnymLtvLknLXsSj/MqJNbcdeoHnRtGTrLOISFGRf1a8vo3q158+stPD13HUnPfMGFfdtw58juJJRj2Yra4MUFG0g7cJinf3aKPuSIiBSTlZWlJFBqPDOjefPm7Nq1q0LlKBEUkWM45/j4ux1MmrmGjbsPMjChKc9eeSqndgzdBcnr1wvnusGdGH9qO38h+4188t0Orji9A7ec240WjesHO8Qqtyv9MM/OX8/IXq04rVOzYIcjIhKSlARKbVAZ9ViJoIgU8dWGPTzy8WqWbd1Ht5aNePHqAQzv2bLG/MfZOCqCO0Z258ozOvLUXG/JialLtvF/Z3Xm/4Z2plH92vvP3hOzf+Bwbj73jOkR7FBEREQkxIUFOwARCQ2rth/gule+4fLnv2LngSweHd+XT24byoherWpMElhYyyZR/HlcHz69fSjDurfgyTlrGTbpM/795Sayc/ODHV6lW7czncmLtjLh9A50bhE6XXdFRKSoTZs20bt37yope968eVx44YUATJ8+nYkTJ1bJdWqC8j7nV199ldTU1DKPufnmmysaWsiovX8aF5GAbPvxEH/79Af+uzSFxvXrce+YHlwzKKHaloKoap1bNOKfE05l6ZYfmfjxah54/3teWriRO0d254I+bWrNkhMTP15Ng4hwbh3eLdihiIjUGtOWpjBp5hpS92USHxvNXaO6M65/22CHFZCkpCSSkpKCHUZglr8Ncx6C/dsgph0MfwD6XlqtIbz66qv07t2b+Pj4ar0uQG5uLvXqVX9aphZBkTrqx4PZ/PmDlZz72Hw+WL6dG4d2ZsHd5/KLs7vUmiSwsP4dmjL5xjN45bqBREeEc8tbS7noH1/wxbrdwQ6twv63fjezV+3kl+d0oXmj2j8WUkSkOkxbmsK9760gZV8mDkjZl8m9761g2tKUCpedm5vLNddcQ9++fRk/fjyHDh3ioYceYuDAgfTu3Zsbb7yRgrW+n3rqKXr16kXfvn25/PLLATh48CDXX389AwcOpH///rz//vvHXKNw69W1117LrbfeyqBBg+jcuTNTp049ctykSZMYOHAgffv25Q9/+EOF763clr8NM26F/VsB532fcau3vYICfc5Tp05l8eLFTJgwgX79+pGZmcmiRYsYNGgQiYmJnHbaaaSnpwOQmprK6NGj6datG3ffffeRazVq1Ij77ruPxMREzjjjDNLS0gDYvHkzw4cPp2/fvgwfPpwtW7YA3ntyxx13cM455/Db3/6WBx98kGuuuYaRI0eSkJDAe++9x913302fPn0YPXo0OTk5FX4exalFUKSOyczO4+UvNvLsvPUczM7lklPacft5JxEfGx3s0KqcmXFO95YM7daCaUtT+NunPzDhxa85q1scvx3dg95tY4IdYrnl5zv+8tEq4mOiuH7wiU8hLSJS1/xxxvesTD1Q6v6lW/aRnVd0KEFmTh53T13OW99sKfGcXvFN+MPYk8u89po1a3jppZcYPHgw119/Pf/85z+5+eabeeCBBwC46qqr+OCDDxg7diwTJ05k48aN1K9fn3379gHw8MMPc+655/Lyyy+zb98+TjvtNEaMGHHca27fvp2FCxeyevVqkpKSGD9+PLNmzWLt2rV88803OOdISkri888/Z+jQoWXeQ8A+vgd2rCh9/7ZFkHe46LacTHj/ZljyWsnntO4DY8ru9hrocx4/fjzPPPMMjz32GAMGDCA7O5vLLruMKVOmMHDgQA4cOEB0tPc5KTk5maVLl1K/fn26d+/OLbfcQvv27Tl48CBnnHEGDz/8MHfffTcvvPAC999/PzfffDNXX30111xzDS+//DK33nor06ZNA+CHH35g9uzZhIeH8+CDD7J+/Xo+++wzVq5cyZlnnsm7777Lo48+ysUXX8yHH37IuHHjyn7e5aAWQZE6Ijcvn8nfbGHYY58xaeYaTu/cjI9/PZRJP02sE0lgYeFhxiWntmPOb87m/gt6siJlPxc+vZDbJi9l695DwQ6vXN5flsJ3KQe4a3T3WtmSKyISLMWTwLK2l0f79u0ZPHgwAFdeeSULFy7ks88+4/TTT6dPnz7MnTuX77//HoC+ffsyYcIEXn/99SPdB2fNmsXEiRPp168fw4YNIysr60hLU2nGjRtHWFgYvXr1OtJaNWvWLGbNmkX//v055ZRTWL16NWvXrq3w/ZVL8SSwrO3lUJ7nXNiaNWto06YNAwcOBKBJkyZHnv3w4cOJiYkhKiqKXr16sXnzZgAiIyOPjM889dRT2bRpEwBffvklV1xxBeAlngsXLjxynZ/+9KeEhx/9v3vMmDFERETQp08f8vLyGD16NAB9+vQ5Ul5lUotgFakJfcpDPUbFVzGF42vaMJJ6YbAzPZtTOsTy9M9O0fICQFREODec1ZmfDmjPc/PX8/IXG/lwxXauPKMjXVo05F/zNoT0+/voJ6tJ3Z9FRLhB7Zv/RkSkSpXVcjd44lxS9mUes71tbDRTfnFmha5dfBI2M+NXv/oVixcvpn379jz44INkZWUB8OGHH/L5558zffp0/vSnP/H999/jnOPdd9+le/fuRcopSPBKUr/+0aEDBd1OnXPce++9/OIXv6jQ/RxXWS13f+/tdwstJqY9XPdhhS5dnudcmHOu1InyCj/H8PBwcnNzAYiIiDhyTuHtx4upYcOiax0XlB0WFlakvLCwsFLLqwglglWgoE95Zk4e4PUp/+27y9m4O4OhJ7UIcnSez3/YxbPzN3DYnz0x1GJUfBVTPL69B7Mx4LrBCTxwYa8aOQtoVYqJjuDu0T24+swEnpj9A698sanI/lB/f3PyHL+b9h0WZiGVrIqI1GR3jepe5PMcQHREOHeN6n6cswKzZcsWvvzyS84880zeeusthgwZwv/+9z/i4uLIyMhg6tSpjB8/nvz8fLZu3co555zDkCFDePPNN8nIyGDUqFE8/fTTPP3005gZS5cupX///uWOY9SoUfz+979nwoQJNGrUiJSUFCIiImjZsmWF7zFgwx/wxgTmFEq6I6K97RUU6HMGaNy48ZFxgD169CA1NZVFixYxcOBA0tPTj3QNLa9BgwYxefJkrrrqKt544w2GDBlS4fuqLEoEq8CkmWuK/KMBcDg3nyfnrOPJOeuCFFXZQj1GxVcxDpj1fVpAYxfqqtYxUUy8pC9zV+9kZ3rRLimh/v5m5uQxaeYaJYIiIpWk4N/Tquj907NnT1577TV+8Ytf0K1bN375y1/y448/0qdPHxISEo50SczLy+PKK69k//79OOe4/fbbiY2N5fe//z233XYbffv2xTlHQkICH3zwQbnjGDlyJKtWreLMM70WzkaNGvH6669XbyJYMDtoFcwaGuhzBm/ylptuuono6Gi+/PJLpkyZwi233EJmZibR0dHMnj37hGJ46qmnuP7665k0aRItWrTglVdeqfB9VRYraBqu6QYMGOAWL14c7DAA6HTPh5T2VP99/WnVGktprn75m1L3hUKMiq9iSovPgI0TL6jeYGqgUP8d1vsrInJiVq1aRc+ePYMdhkilKKk+m9kS59yAQM5Xi2AViI+NLrVPeSh0KwMvllCOUfFVTGnx1bVJYU5UqP8O6/0VERGRitKsoVXgrlHdiS42e19l9SmvLKEeo+KrmFCPL9SF+vML9fhEREQk9KlFsApUZZ/yyhLqMSq+ign1+EJdqD+/UI9PRCSUHW9GSJGaojKG92mMoIiIiIjUCRs3bqRx48Y0b95cyaDUWM459uzZQ3p6Op06dSqyT2MERURERESKadeuHdu2bWPXrl3BDkWkQqKiomjXrl2FylAiKCIiIiJ1QkRExDEtKCJ1lSaLERERERERqWOUCIqIiIiIiNQxSgRFRERERETqmFoza6iZ7QI2l3FYDLA/wCIDPbas4+KA3QFesyYrz7OtqTFUVvkVLae85wej3kPdqPuq99VXVlXW+0CPV733hEK9h6qNo6bW+/Keo3ofuLpQ7yuz/Jpe7wM5LlTrfUfnXIuAjnTO1Zkv4PnKPras44DFwb7vUHu2NTWGyiq/ouWU9/xg1Hv/mFpf91Xvq6+sqqz3gR6vel/5dSJU46ip9b6856jeB6dOhHIcofBZJxTqfSDH1YZ6X9e6hs6ogmPLU2ZtFgrPoapjqKzyK1pOec9Xva86ofAcakq9r2hZVVnvAz0+FN7vUBAqz6Eq46ip9b6856jeBy5UnkNN+Te/ptf7E42jRqk1XUNDlZktdgEu6ihSm6juS12kei91keq91EW1od7XtRbBYHg+2AGIBInqvtRFqvdSF6neS11U4+u9WgRFRERERETqGLUIioiIiIiI1DFKBEVEREREROoYJYIiIiIiIiJ1jBJBERERERGROkaJYJCZWUMzW2JmFwY7FpHqYGY9zexZM5tqZr8Mdjwi1cXMxpnZC2b2vpmNDHY8ItXBzDqb2UtmNjXYsYhUJf8z/Wv+v/MTgh1PIJQIniAze9nMdprZd8W2jzazNWa2zszuCaCo3wJvV02UIpWrMuq9c26Vc+4m4FKgRq+/I3VHJdX9ac65/wOuBS6rwnBFKkUl1fsNzrmfV22kIlWjnL8DPwGm+v/OJ1V7sCdAieCJexUYXXiDmYUD/wDGAL2An5lZLzPrY2YfFPtqaWYjgJVAWnUHL3KCXqWC9d4/JwlYCMyp3vBFTtirVELd993vnycS6l6l8uq9SE30KgH+DgDtgK3+YXnVGOMJqxfsAGoq59znZpZQbPNpwDrn3AYAM5sMXOScewQ4puunmZ0DNMSrRJlm9pFzLr9KAxepgMqo934504HpZvYh8GbVRSxSOSrp33wDJgIfO+e+rdqIRSqusv7NF6mpyvM7AGzDSwaTqSGNbUoEK1dbjv4lALwKcXppBzvn7gMws2uB3UoCpYYqV703s2F43SfqAx9VaWQiVatcdR+4BRgBxJhZV+fcs1UZnEgVKe+/+c2Bh4H+ZnavnzCK1GSl/Q48BTxjZhcAM4IRWHkpEaxcVsI2V9ZJzrlXKz8UkWpTrnrvnJsHzKuqYESqUXnr/lN4HxREarLy1vs9wE1VF45ItSvxd8A5dxC4rrqDqYga0WxZg2wD2hd63Q5IDVIsItVF9V7qKtV9qYtU76WuqzW/A0oEK9cioJuZdTKzSOByYHqQYxKpaqr3Ulep7ktdpHovdV2t+R1QIniCzOwt4Eugu5ltM7OfO+dygZuBmcAq4G3n3PfBjFOkMqneS12lui91keq91HW1/XfAnCtzCJuIiIiIiIjUImoRFBERERERqWOUCIqIiIiIiNQxSgRFRERERETqGCWCIiIiIiIidYwSQRERERERkTpGiaCIiIiIiEgdo0RQREQqjZn93cxuK/R6ppm9WOj142Z2Rxll/C+A62wys7gStg8zs0GlnJNkZveUUW68mU31f+5nZueX8/xrzewZ/+ebzOzqsu6lrHs40XKqgh/bB8GOQ0REKq5esAMQEZFa5X/AT4EnzCwMiAOaFNo/CLitpBMLOOdKTOQCNAzI8OMoXu50YHoZ104Fxvsv+wEDgI8CPb9YWc8Gemwxwyh0DxUoR0REpFRqERQRkcr0BV6yB3Ay8B2QbmZNzaw+0BNYCmBmd5nZIjNbbmZ/LCjAzDL872Fm9k8z+97MPjCzj8xsfKFr3WJm35rZCjPrYWYJwE3A7WaWbGZnFQ6sWGvdq2b2lJn9z8w2FJRrZglm9p2ZRQIPAZf5ZV1W7PyxZva1mS01s9lm1qr4gzCzB83sTr+VMbnQV56ZdSypjJLuoaAcv8x+ZvaV/8z+a2ZN/e3zzOyvZvaNmf1Q/N79Y9qY2ed+ud8VHGNmo/3nuMzM5vjbTvOfzVL/e/cSymtoZi/77+FSM7uo1FohIiIhR4mgiIhUGr9FLdfMOuAlhF8CXwNn4rWuLXfOZZvZSKAbcBpey9upZja0WHE/ARKAPsANfhmF7XbOnQL8C7jTObcJeBb4u3Oun3NuQRnhtgGGABcCE4vdRzbwADDFL2tKsXMXAmc45/oDk4G7S7uIcy7VL6Mf8ALwrnNuc0llBHAP/wZ+65zrC6wA/lBoXz3n3Gl4La5/4FhXADP9OBKBZDNr4cd0iXMuEa81F2A1MNSP7QHgLyWUdx8w1zk3EDgHmGRmDUt7DiIiElrUNVRERCpbQavgIOBvQFv/5/0c7bI50v9a6r9uhJcYfl6onCHAO865fGCHmX1W7Drv+d+X4CWN5TXNL3tlSS16ZWgHTDGzNkAksLGsE8xsMF5CW9BaV64yzCwGiHXOzfc3vQa8U+iQws8joYQiFgEvm1kE3r0nm9kw4HPn3EYA59xe/9gY4DUz6wY4IKKE8kYCSQWtlUAU0AFYdbz7EBGR0KAWQRERqWz/w0v8+uB1Df0KrzVvEF6SCGDAIwUtZc65rs65l4qVY2Vc57D/PY8T+8Pm4UI/l3Wt4p4GnnHO9QF+gZcElcpP9l4CLnPOZZxIGQE47vNwzn0ODAVSgP/4E9AYXqJX3J+Az5xzvYGxpcRmeC2JBe9hB+eckkARkRpCiaCIiFS2L/C6W+51zuX5rUyxeMngl/4xM4HrzawRgJm1NbOWxcpZCFzijxVshTeJSlnSgcaVcA9llRWDl1ABXHO8QvwWuLfxunT+EEAZJV7XObcf+LHQ+L+rgPnFjztOHB2Bnc65F/CS0lPw3o+zzayTf0yzEmK7tpQiZ+KN0zT/3P6BxiIiIsGnRFBEpJYwswwz6xzsOPDGrsXhtQQW3rbfObcbwDk3C3gT+NLMVgBTOTb5eRfYhteq+BzeWMP9ZVx7BnBxSZPFnIDPgF4Fk8UU2/cg8I6ZLQB2l1HOIGAg8MdCE8b8P39fSWUUv4d+QMHx1+CNxVvub3+orJswsw7+BDzn4I0LXApcAjzpnNsF3Ai8Z2bLgPf9Yx8DHjGzL4DwYkWO9uvZn/C6jC43s+/814Wvm2Bmzszq+a8/NrPjJs0nwrzJhIZVdrmVzZ9saGGw4xARKWDOldQjRESk7jGzTUArvK51OXhdHG9yzm2thHJvcM7NLmX/MOB151y7ilynNjKzRs65DDNrDnwDDHbO7Qh2XNXJzK7Fqz9DStk/D6/+vFjS/gpe+4TL9mdA3QhEOOdyKymeV4Ftzrn7K6O86lTW+ygiUt3UIigiUtRY51wjvBkl0/DGcQVdQatKbVTGvX1gZsnAAuBPdS0JFBERqSpKBEVESuCcy8LrrtirYJuZ1Tezx8xsi5mlmdmzZhbt74szb627fWa218wW+GPb/oM3k+IMv+tmkWUG/On2Pwbi/f0Z5q0796CZTTWz183sAHCtv7bbl/41tpvZM+atd1dQljOzrv7Pr5rZP8zsQzNLN2+9ui6l3a+ZvWNmO8xsv3lrzZ1caF+0mT1uZpv9/QsL3fcQ89aZ22dmW/1Wj4J17W4oVEaRbnF+rP/PzNYCa/1tT/plHDCzJWZ2lnNumL/cQR//Ga3372eJmbX37/HxYvcyw8yOWbTef78eK7btfTO7w//5t2aW4pe/xsyGl1BGJ/9ew/zXL5rZzkL7Xy+4tpnFmNlL/nuVYmZ/NrPwUp7HSP+a+81bO3F+4efnH/OYmf1oZhvNbIy/7WG8WUif8evOMyXEXLyL5jwz+5OZfeHf6ywziyt+bGllF6tnF5i3huAB/717sPj1C8VxpE6Yt2ZhRqEvZ373ztLqopndCEwA7vbPmeFv32RmI/yf65vZE2aW6n89Yd76lZjZMDPbZma/MbOd/vty3XHivda8NSbT/Wc+odC+/zOzVf6+lWZ2ir/9nkJ1dKWZXXyc8nuY2afm/XuxxswuLe1YEZGqoERQRKQEZtYAuIyi49z+CpyENzarK96yCA/4+36DN56tBV730t8Bzjl3FbAFv6XROfdo4es45w4CY4BUf38jfy0+gIvwktFY4A28Lqu3442/OxMYDvzqOLfxM+CPQFNgHfDwcY79GG/5hpbAt/71CjwGnIo31q0Z3pp5+eatFfgxXqtpC/+5JB/nGsWNA07naLK9yC+jGd74wXfMrGC2yjv8+zkfaAJcDxzCW0LhZ4USszi85/JWCdd7E2+B+ILJTZriLYEw2bwF028GBjrnGgOjgE3FC/CXWTgAFEyMchaQYWY9/ddDOTqBy2tALl5d6e9fq0hyVyjmqcC9QHNgDd6zLux0f3sc8CjwkpmZc+4+vNbSm/26c3MJ912SK4Dr8N7vSODO4gcEWPZB4Gq8OnoB8EszG1fWxZ1ziQX1He+9XYNX76CUuuice97/+VH/3LElFH0fcAZePUrEW6eycDfS1ngT4bQFfg78w68HRZj3B5qngDF+fRiEX7fN7Kd4Y0SvxquLScAe/9T1eHUiBu9373XzZowtqfxP8epkS7y6/U8r9AcYEZGqpkRQRKSoaWa2D+/D/nnAJAA/efg/4Hbn3F7nXDreItuX++fl4HUn7eicy3HOLXAVH4T9pXNumnMu3zmX6Zxb4pz7yjmX6y88/hxw9nHOf885940/PusNvA/HJXLOveycS3fOHcb7kJvot2iF4SVdv3bOpfizgP7PP24CMNs595Z/z3ucc+VJBB/xn2WmH8Prfhm5zrnHgfpAd//YG4D7nXNrnGeZf+w3eBPIFLTeXQ7Mc86llXC9BXhLJRRMIjMe7xmn4iXZ9fEmh4lwzm1yzq0vJe75eDNttvZfT+XozJtNgGXmzXI6BrjNOXfQObcT+DtH60th5wPfO+fe89+rp4DiXWA3O+decM7l4SWYbfD+4HCiXnHO/eA/+7c5Tt04HufcPOfcCr+OLsdLwI9XJ4swsyHAn4Ek59wBv8wS62KARU4AHnLO7fQnwvkj3uyqBXL8/TnOuY+ADI7WseLygd5mFu2c2+6c+97ffgNeMrrIr4vrnHOb/djfcc6l+s9jCl5r92kllH0hsMk594pf37/FmxxpfID3KSJSYUoERUSKGueci8VLCm4G5vsf+FsADYAl5nUN3Ad84m8HL2FcB8zyu5PdUwmxFJmkxsxOMq/76Q7zuov+Ba+FqDSFk4lDeIu2H8PMws1sot+l7QBHW8Li/K8ovJaO4tqXsj1Qxe/vN353u/3+843h6P0d71qvAVf6P18J/Kekg/zEfDJe6wt4rWIFrU3rgNvwEo+dZjbZzOJLud58vKUshgKfA/Pwkp+zgQXOW6S+I96MmtsL1Zfn8Fp/ioun0LPw49xW7JgdhfYf8n8s8f0MUEB1oyxmdrqZfWZmu8xsP3ATx6+Thc9tj5eEXuP8ZTXKqIuBiAc2F3q92d9WYE+xiWtKvHe/pf4yvPvZbl4X6x7+7lLropldbd5srwXvee9SYu8InF5wnH/sBLwWSxGRaqFEUESkBH7L13t4LUVD8Kb3zwROds7F+l8xftc2/BaM3zjnOuMtwH2HHR1jVlbLYGn7i2//F7Aa6Oaca4LX/bS8C6GX5Aq8bqgj8JKvBH+74d13FlDS+MKtpWwHr8tgg0KvS/qAe+T+zFsm4bfApUBTPxnfz9H7O961XgcuMrNEoCcwrZTjwGuxGm/emnqn47XCeME496bzZnTs6Mf211LKmI/XqjjM/3khMBgvESzoFroVb4H3uEL1pYlzrqSuf9uBIzPG+q3P5ZlBtiqn/y6r7DeB6UB751wM8CwB1EnzxphOA55wzn1caNfx6mIg8aTivX8FOvjbys05N9M5dx5e6+tq4AV/V4l10a9TL+D9Aam5X4e/o+TnsRWYX6huxPrdXX95IrGKiJwIJYIiIiUwz0V44+tW+a08LwB/N3/hc/MWQR/l/3xIF0M3AAAgAElEQVShmXX1P8QfwEsg8/zi0oDjre+XBjQPoPtbY7/sDL91orI+NDbGS1r24CVvfynY4d/3y8DfzJvEJtzMzvQn4HgDGGFml5o3uUhzMyvoYpgM/MTMGpg3scjPA4ghF9gF1DOzB/C6WRZ4EfiTmXXz35u+5i0pgXNuG974wv8A7xZ0NS2Jc26pf40XgZnOuX0AZtbdzM717ysLL+nPK6WMtf7+K4HP/S6NaXhr8833j9kOzAIeN7Mm5k0c1MXMSuo2+SHQx8zGmTehy/+jfC1DZdWviiir7MbAXudclpmdhpfIBeJlYLUrNmaW49TFAON5C7jfzFr4Yy8fwPtDQbmYWSszS/LH8h3G60JaUB9eBO40s1P9utjVTwIb4iWqu/wyrsNrESzJB8BJZnaVmUX4XwPt6FhTEZEqp0RQRKSoGeYtqH0Ab3KVawqNDfotXvfPr/xua7M5Or6om/86A/gS+Kdzbp6/7xG8D6f7zKykSTlW432A3eAfU1qXxDvxPmin4yWlUyp0p0f9G68LXQqwkqIT5BRcdwVesrUXr6UszDm3BW9822/87cl4E3SANx4uG++D+2sUnXymJDPxJgn5wY8li6JdR/+G141wFt578xIQXWj/a3gzi5bYLbSYt/BanN4stK0+MBGvBXQHXhfO3x2njPl43Qy3FHptwNJCx1yNNxHLSuBHvLGEx0wc4pzbDfwUbxKYPXiT5yzGS0AC8SReK+ePZvZUgOcEqqyyfwU8ZGbpeEnX2wGWezlwsRWdOfQsyq6LL+GN49xnZiW1/P4Z79ktx6uz3/rbyisMr16n4tXts/EnZnLOvYP3b8ObeL+L04BmzrmVwON4v/9pePXxi5IKd94Y45H+c0jFq3N/xauHIiLVQgvKi4hIjWdmQ/FafhL8Vsway7wJerYBE5xznwU7HhERqZ3UIigiIjWamUUAvwZerKlJoJmNMrNYv2tqwdjP4q1hIiIilUaJoIiI1Fj+mKp9eF0unwhyOBVxJt5MlLvxJhsad7yxjiIiIhWlrqEiIiIiIiJ1jFoERURERERE6ph6wQ6gssTFxbmEhIRghyEiIiIiIhIUS5Ys2e2caxHIsbUmEUxISGDx4sXBDkNERERE/j979x0eVbU1cPi300MqLbQEEjoxREpCC9KlSJEmFkAQKYoI9quf5SLqFcUKiooIgqCIiFQRpFeBQJDeWwolQArpbX9/nACBTCiZZCZlvc+TJ5kz+5yzJkDImrX32kIIq1BKnbnbsTI1VAghhBBCCCFKGUkEhRBCCCGEEKKUkURQCCGEEEIIIUqZErNGUAghhBBCCGF96enpREREkJKSYu1QSiwnJye8vb2xt7fP9zUkERRCCCGEEEIUmIiICNzc3PD19UUpZe1wShytNZcvXyYiIgI/P798X0emhgohhBBCCCEKTEpKCuXLl5cksJAopShfvrzZFVepCAohhBBCiOsWhUUyaeURomKTqerpzKtd6tG7cTVrhyWKGUkCC1dBfH8lERRCCCGEEICRBL6xcB/J6ZkARMYm88bCfQCSDApRwsjUUCGEEEIIAcCklUeuJ4HXJKdnMmnlEStFJET+nD59moCAgEK59vr16+nRowcAS5YsYeLEiYVyn8JmlYqgUqor8CVgC0zXWk+85fnPgfbZD8sAXlprT8tGKYQQQghRukTFJt/TcSEKQnGejtyrVy969epl7TDyxeIVQaWULfA10A3wBx5XSvnnHKO1flFr3Uhr3QiYAiy0dJxCCCGEEKVNFU8nk8fLONqSkJph4WhEaXBtOnJkbDKaG9ORF4VFmn3tjIwMhgwZQmBgIP379ycpKYkJEyYQHBxMQEAAI0eORGsNwOTJk/H39ycwMJDHHnsMgMTERIYNG0ZwcDCNGzdm8eLFue7x448/MmbMGACGDh3K2LFjadWqFTVr1mTBggXXx02aNIng4GACAwP573//a/ZrKwjWqAg2A45rrU8CKKXmAQ8DB/MY/zhQNL5bQgghhBAl2P3enkTFnr/pmK2NIjE1k06fbuCdnv50C6gsjUDEXXt36QEORsXn+XzY2VjSMrNuOpacnslrC/byy46zJs/xr+rOf3ved8d7HzlyhB9++IGQkBCGDRvG1KlTGTNmDO+88w4AgwcPZtmyZfTs2ZOJEydy6tQpHB0diY2NBeCDDz6gQ4cOzJgxg9jYWJo1a0anTp1ue89z586xefNmDh8+TK9evejfvz+rVq3i2LFj7NixA601vXr1YuPGjbRp0+aOr6EwWWONYDUgPMfjiOxjuSilagB+wNo8nh+plApVSoVGR0cXeKBCCCGEEKXFxqPR/HXgPEE1PKnm6YQCqnk68+kj97NwdCvKujgweu5uhs7cyelLidYOV5QQtyaBdzp+L3x8fAgJCQFg0KBBbN68mXXr1tG8eXMaNmzI2rVrOXDgAACBgYEMHDiQOXPmYGdn1MpWrVrFxIkTadSoEe3atSMlJYWzZ00np9f07t0bGxsb/P39uXDhwvXrrFq1isaNG9OkSRMOHz7MsWPHzH595rJGRdDUW0g6j7GPAQu01pmmntRaTwOmAQQFBeV1DSGEEEIIcRtRscmMmxdGHS9XZj/dnDIOuX9FXDomhNnbzvDZ30fp/MVGnmtXm1Fta+Jkb2uFiEVxcafKXcjEtUSaWINazdOZX0e1NOvet1aulVKMHj2a0NBQfHx8GD9+/PW9+JYvX87GjRtZsmQJ7733HgcOHEBrze+//069evVuus61BM8UR0fH619fm3aqteaNN95g1KhRZr2egmaNimAE4JPjsTcQlcfYx4BfCj0iIYQQQohSKi0ji9Fzd5OeqflmUFOTSSCAna0Nw1r7sfqltjzoX4nPVx+l25eb2HRMZmWJ/Hu1Sz2cb3kzwdnelle71MvjjLt39uxZtm3bBsAvv/xC69atAahQoQIJCQnX1/BlZWURHh5O+/bt+fjjj4mNjSUhIYEuXbowZcqU6wldWFhYvuLo0qULM2bMICEhAYDIyEguXrxo7sszmzUqgjuBOkopPyASI9l74tZBSql6QFlgm2XDE0IIIYQoPT5YfpA94bFMHdiEWhVd7zi+socTXz/RhEeDonln8X4G/7CDHoFVeLuHP5XcTTebESIv17qDFkbX0AYNGjBr1ixGjRpFnTp1ePbZZ4mJiaFhw4b4+voSHBwMQGZmJoMGDSIuLg6tNS+++CKenp68/fbbvPDCCwQGBqK1xtfXl2XLlt1zHJ07d+bQoUO0bGlUOF1dXZkzZw5eXl5mv0ZzqGsZrkVvqtRDwBcY20fM0Fp/oJSaAIRqrZdkjxkPOGmtX7+bawYFBenQ0NDCClkIIYQQosRZvCeScfP2MLy1H2/18L/zCbdISc/k2w0nmLr+BA62Nrz0YF2ebFkDO1vZqro0O3ToEA0aNLB2GCWeqe+zUmqX1jrobs63SiJYGCQRFEIIIYS4e8cuXKXXV1sIqObOzyNaYG9G8nb6UiLvLDnAxqPR+Fdx5/0+ATSpXrYAoxXFiSSClmFuIihv1wghhBBClDIJqRk8M2cXLo62fPVEE7OSQADfCi7MeiqYr59owuXEVPp9s5U3Fu4jNimtgCIWQhQ0SQSFEEIIIUoRrTWv/76XU5cSmfx44wJb16eUontgFda83I5hIX7MDw2n46cbWLArgpIyA02IkkQSQSGEEEKIUuTHradZtvccr3SpR6taFQr8+q6Odrzdw5+lY1pTo3wZXvntXx797h+OXrha4PcSQuSfJIJCCCGEEKXErjMxfLD8EJ0aePFMm1qFei//qu4seKYVE/s25OjFqzz05SY+XHGIpLSMQr2vEOLuSCIohBBCCFEKXE5I5bm5u6nq6cynjzTCxkbd+SQz2dgoHmtWnbUvt6Nvk2p8t+EknT7dwMoD52W6qBBWJomgEEIIIUQJl5mlGTsvjCtJaUwd2ASPMvYWvX85Fwc+7n8/vz3TEjcne0b9tIvhs0IJv5Jk0ThE6XH69GkCAgLuevyPP/5IVFTUHceMGTPG3NCKDEkEhRBCCCFKuC9WH2XL8cu89/B9BFTzsFocwb7lWDa2NW8+1IBtJy/z4Ocb+HrdcdIysqwWkygC9s6HzwNgvKfxee98i4dwN4lgYcnIsM50aTur3FUIIYQQQljE2sMXmLL2OAOCvHk0uLq1w8He1oYRbWrSPbAKE5YeZNLKIyzcHcF7vQMKpXmNKOL2zoelYyE92XgcF248BggcYNalMzIyGDJkCGFhYdStW5fZs2fzySefsHTpUpKTk2nVqhXfffcdv//+O6GhoQwcOBBnZ2e2bdvG/v37GTduHImJiTg6OrJmzRoAoqKi6Nq1KydOnKBPnz58/PHHALi6ujJu3DiWLVuGs7MzixcvplKlSpw5c4Zhw4YRHR1NxYoVmTlzJtWrV2fo0KGUK1eOsLAwmjRpgpubG6dOneLcuXMcPXqUzz77jH/++YcVK1ZQrVo1li5dir19wVbyZUN5IYQQQogSKvxKEj2mbKaapzMLR7fCyd7W2iHlsu7wRd5Zsp/wK8n0blSVN7v7U9HN0dphCTPctNH5itfh/L68B0fshMzU3MdtHcE72PQ5lRtCt4m3jeH06dP4+fmxefNmQkJCGDZsGP7+/gwbNoxy5coBMHjwYAYMGEDPnj1p164dn3zyCUFBQaSlpVG/fn1+/fVXgoODiY+Pp0yZMsyZM4cJEyYQFhaGo6Mj9erVY/Pmzfj4+KCUYsmSJfTs2ZPXXnsNd3d33nrrLXr27En//v0ZMmQIM2bMYMmSJSxatIihQ4dy6dIlFi9ejK2tLePHj2f16tWsW7eOgwcP0rJlS37//Xe6detGnz59GDJkCL179877+5xNNpQXQgghhCjlUtIzGT13N1la882gJkUyCQRoX9+Lv19sy9gOtflz33k6fLqen7adJjOrZBQrxB2YSgJvd/we+Pj4EBISAsCgQYPYvHkz69ato3nz5jRs2JC1a9dy4MCBXOcdOXKEKlWqEBxsJKLu7u7Y2RkTKTt27IiHhwdOTk74+/tz5swZABwcHOjRowcATZs25fTp0wBs27aNJ554AjASz82bN1+/zyOPPIKt7Y1/l926dcPe3p6GDRuSmZlJ165dAWjYsOH16xUkmRoqhBBCCFECvbv0IPsi4/j+ySBqlHexdji35WRvy0ud6/Fw42q8s3g/by8+wG+7Ini/dwCB3p7WDk+Y4w6VOz4PMKaD3srDB55abtatlVK5Ho8ePZrQ0FB8fHwYP348KSkpuc7TWuc69xpHxxvValtb2+vr++zt7a+fk/P47WJycbn53+W1a9vY2Nx0PRsbm0JZRygVQSGEEEKIEub3XRH8suMsz7StxYP+lawdzl2rVdGVOU8358vHGnEuLoWHv97CO4v3E5ecbu3QRGHp+A7YO998zN7ZOG6ms2fPsm3bNgB++eUXWrduDUCFChVISEhgwYIF18e6ublx9epVAOrXr09UVBQ7d+4E4OrVq/lOxFq1asW8efMAmDt37vUYigKpCAohhBBClCCHzsXz5qJ9tKhZjlc617V2OPdMKcXDjarRvr4Xn606yuxtp/lz33ne6t6AhxtVzbNSI4qpaw1h1kyAuAjw8DaSQDMbxQA0aNCAWbNmMWrUKOrUqcOzzz5LTEwMDRs2xNfX9/rUT4ChQ4fyzDPPXG8W8+uvv/L888+TnJyMs7Mzq1evzlcMkydPZtiwYUyaNOl6s5iiQprFCCGEEEKUEPEp6fSaspmktEyWj32gRDRd2R8Zx5uL9vNveCytapWnbd2KzN52hqjYZKp6OvNql3r0blzN2mGKHEw1MREFz9xmMVIRFEIIIYQoAbTWvPbbXsJjkvllRIsSkQQCBFTzYOGzrfhlx1neX3aArScuX38uMjaZNxYaHSklGRTi3sgaQSGEEEIISyqkzbOnbzrFXwfO80a3+jTzK1cg1ywqbG0Ug1rUwLNM7uQ2OT2TSSuPWCEqIYo3qQgKIYQQQlhKIW2evf3kZSb+dZhuAZV5urVfAQRaNF2Iz93hESAqNtnCkYg7uV3nTWG+gljeJxVBIYQQQghLWTPhRhJ4TXqycTyfLl5NYcwvYVQvV4aP+weW6F++q3o6mzzuZG9LQmrBt9cX+ePk5MTly5cLJFkRuWmtuXz5Mk5OTmZdRyqCQgghhBCWEheRx/FwWPkm1HkQqrcEu7tb35eRmcXzP4dxNSWdn55uhpuTfQEGW/S82qUebyzcR3J65vVjdjaK5PRMek3ZzNRBTahf2d2KEQoAb29vIiIiiI6OtnYoJZaTkxPe3t5mXUMSQSGEEEKIwnb1Aqz8PyCPComdI+yYBtu+AnsX8GsDtTsaiWFZ3zwvO2nVEbafusJnA+4vFQnQtYYwk1YeualraCV3J8bOC+Phr7bwXu8ABgT5WDnS0s3e3h4/v5I7RbmkkO0jhBBCCCEKS1YW7JoJq9+FjGSo0wWOrza+vsbeGXpOhvrd4dQm4/njf0PMaeP58rWh9oNQuxP4hlzffHvVgfOM/GkXA5tX54M+DS3/2oqY6KupjJsXxtYTl+nf1Jv3Hg7A2cHW2mEJYVH3sn2EJIJCCCGEEIXh/H5Y9gJE7ATfB6DH51ChjtEw5k6bZ2sNV07Csb+NxPD0JshIATsn8G3N5SptGLLJHZvytfnt2VY42knCA5CZpfly9VGmrDtOXS83vh7YhNpertYOSwiLkURQCCGEEMJa0hJh/YewbSo4l4UuH0Dgo2BOE5f0ZDizBY6tJuv4amwuHwMgw706dvU6G9VCvzbg4FJAL6IIu4tEesPRaF78dQ8p6Zl82LchDzeSPQZF6SCJoBBCCCGENRxZAX++ajR/afIkdHoXyhTcnn5aa15dsJcdu3fxfUgc9eK3w6mNkJ4Itg5Go5k62dNIK9Y3L/ksim7dfgNuTK29JRk8F5fM8z+HEXomhoHNq/N2D3+c7KVyKko2SQSFEEIIISwpLhJWvAaHl0HFBsY00BotC/w283ac5fWF+xjboTYvda5nHMxIhbPbjCmkx1ZD9CHjuLv3jYYzfm3BqZg0k0lPMap9ceE3f449C2e3QlZm7nM8fODF/bkvlZnFJyuP8N3Gk9xX1Z2pA5tQo3wpqJqKUksSQSGEEEIIS8jMMLp9rvvASFDavgYtx4CdQ4Hfan9kHH2/2Upzv3L8+FQzbG3yqPbFRWQ3nFkNJzdAajzY2IFPixuJYaUA61QLtYakK9nJ3bUEL/zmpC/x1i0HFLhVAU8fCN+ex4UVjI/N87Z/H7zAy/P3oIFJ/e+na0DlgnpFQhQpkggKIYQQQhS2yF2w7EU496/R1bP7J7fd6sEccUnp9PhqExmZmmXPt6a8693tM0hmOoTvMLqQHl8N5/cZx10rG9NHa3eEWu2NtYzX3E0zm9vdLz4yO6nLmeSF3ziWnnTzOXbORpLn4W1U9jx8cjz2BvdqYJu9P+LnAca1TKnfA7p9ZJxjQviVJJ77eTd7I+J4urUf/+laHwc7m7t7XUIUE0U+EVRKdQW+BGyB6VrriSbGDADGY2y486/W+onbXVMSQSGEEEJYREocrH0fdnwPrpWg20Tw711oFbasLM2I2aFsPBbNr6Na0qR62TuflJer5+H4GiMxPLHWeC3KBryDjcQQYPNnea/BS4nLO8GLDYer58i1V6JLxewEz/uWJC876StT7u6/d3mtEazTDY6uMF5Lu/9Ai9E3ksccUjMy+fDPw/y49TSNq3vy1RNNqObpfG/fQyGKsCKdCCqlbIGjwINABLATeFxrfTDHmDrAfKCD1jpGKeWltb54u+tKIiiEEEKIQqU1HFwEK16HhAvQbAR0eAucPAr1tl+vO86klUcY39OfoSEFuEl3ZoZR1by2b2FUWN5jbeyNhCs1/ubjtg5Gxc7DGzyr50jwrn2udn3fwwKTV8Uy5gz89Toc+TN7neZnUKOVyUss33uO//y+FztbxecDGtG+vlfBxiiElRT1RLAlMF5r3SX78RsAWusPc4z5GDiqtZ5+t9eVRFAIIYQQhSbmNCx/xUiYKgdCzy+gWtNCv+3W45cY9MN2ugdWZfJjjVCFua4vIRo+qZ33881G3kjyriV9Ll5gU8SmVx7+02jcExcOjQbCgxPApUKuYSejExg9dzeHz19ldLtavPRgXexsi9hrEeIe3UsiaFfYwZhQDcg5uTsCaH7LmLoASqktGNNHx2ut/7JMeEIIIYQQ2TLTYesU2PAx2NhClw+NhMi28H+FOh+XwvO/hFGzoisT+zYs3CQQwDV7CqepNXgePvDQpMK9f0Gp/xDUbAsbJxl/doeXQ6fx0GTITUlrzYquLHouhPFLDjB1/QlCz8Qw5fHGVHJ3slroQliSNd72MPVT7NaypB1QB2gHPA5MV0p55rqQUiOVUqFKqdDo6Fs7TAkhhBBCmOHsP/DtA7DmXaOpynM7oOVoiySB6ZlZjPl5N8npmXw7qAkujhZ6777jO7mncto7G8eLEwcXI/l7ZovRIXXZCzCjM5zbe9MwJ3tbJvYL5NNH7mdfRBzdJ29iy/FLVglZCEuzRiIYAfjkeOwNRJkYs1hrna61PgUcwUgMb6K1nqa1DtJaB1WsWLHQAhZCCCFEKZJ0BZY8DzO6QFoCPD4PHptrrHezkIkrDhN6JoaP+gVS28vNYvclcIDRGMbDB1DGZxObtRcbXvVh6DLo8x1cOQXT2hprPFNuXuvYr6k3i8eE4FnGgUE/bOfL1cfIzCoZnfWFyIs11gjaYTSL6QhEYjSLeUJrfSDHmK4YDWSGKKUqAGFAI6315byuK2sEhRBCCGEWrWHvr7DyTUiOMap/bV8HR1eLhrF87zme+3k3Q1v5Mr7XfRa9d4mWHGM0mQmdCW6Vocv/4L4+N3UsTUzN4M0/9rFoTxQP1KnAF482uvutOoQoAu5ljaDFK4Ja6wxgDLASOATM11ofUEpNUEr1yh62ErislDoIrANevV0SKIQQQghhlkvHYXYv+GMUlPODURug8/sWTwJPRCfw2oJ/aVzdk/97qIFF713iOZeFHp/D8DXg6gULnoI5feHyietDXBzt+PzRRnzYtyHbT12h++TN7Dx9xYpBC1F4ZEN5IYQQQpRe6Smw+XNj7zw7Z+j0X2j6lFU6YSalZdD76y1cSkhj+djWVPGQ/e0KTVYm7PwB1r4HGanQ+kXjw/5Go5gDUXGMnrubiJhkXutSjxEP1MTGppAb9ghhpiJdERRCCCGEKBJOboBvQ2DDRGjQC8bshOCnrZIEaq35v4X7OHYxgS8fayRJYGGzsYXmI40/8wY9jb8DU1sYeypmu6+qB0ufb01n/0p8uOIwI38KJTYpzYpBC1GwJBEUQgghROmSEA0LRxpTQbMyYdBC6P8DuFWyWkhztp9l0Z4oXupUlwfqSAM8i3GrbPzZD15kJIdz+sH8IRBv9DF0d7Jn6sAm/LenPxuORtN98mb2hMdaOWghCoYkgkIIIYQoHbKyYNeP8FUQ7F8IbV6F0duMrSGsaE94LBOWHqB9vYo81/42G7qLwlOrPTy7Fdq/BUf/gq+CYdvXkJmBUoqnQvyYP6olAI98u5Uft5yipCyvEqWXrBEUQgghRMl34SAsexHC/4EaIUbTkIr1rB0VMYlp9JiyGYDlY1vjWcbByhEJrpyCFa/BsVVQqSH0+Ax8mgEQm5TGy/P/Zc3hi3RvWIWJ/Rri5mRv5YCFuOFe1ghKIiiEEEKIkmXvfGObgLgIcK9mbCh+YjU4uhudQBs9cdOWAZa2KCySSSuPEBWbjIOdDRmZWfzxXAiB3p5Wi0ncQms4tBT+eh3iI6HJk9DpXShTjqwszbRNJ5m08gg+ZZ2ZOrAp/lXdrR2xEIA0ixFCCCFEabV3PiwdC3HhgIb4CDj2F3g3hzGh0Hig1ZPANxbuIzI2GQ2kZmRhY6M4GZ1otZiECUqBfy94bge0HANhc40pxWFzsEHzTNta/DKiBcnpmfSeuoVfdpyVqaKi2JGKoBBCCCGKl8x0SIyGhIvGR+JFSLhgNIHZ/SOkJ+c+x8MHXtxv8VBvFTJxLZGxueOr5unMltc7WCEicVcuHIBlLxlTi31aGNNFK93HpYRUXpi3h83HL9G3cTXe7xNAGQc7a0crSrF7qQjK31QhhBBCWF9mhpHcJV40ErqEC9lf50z2sj+S89jg28HNdBIIxjTRIiDKRBJ4u+OiiKh0Hzy1Av79GVa9Dd8+AC1HU6Ht68wa1owpa4/x5Zpj7IuMo1+Tavz0z1miYpOp6unMq13q0btxNWu/AiFykURQCCGEEPcm5xo8D2/o+A4EDsg9LjMDki7nSOquJXjZnxMu3vg66QpgYpaSgyu4VATXSlChjtHoxbUSuGYfc/EyvnbxAocy8HlA9rTQW3h4F/i3IT+qejqbrAhW9ZR9A4s8GxtoPAjqPQSr/wtbp8D+hdh2ncgLHXsSVKMco34KZeJfR66fEhmbzBsL9wFIMiiKHLOmhiqlymmt83hbzrJkaqgQQghhAdfW4OWsvNnYQ812UKbcjSmaiRch8RImkzv7MuDqlZ3EXfuolJ3w3fK1g4v58dk7Q8/JppNVC/tjVwQv/fbvTd8VZ3tbPuzbUBKF4iZ8hzFd9MI+qNMZun1Mi29Pcj4+JddQmforLMWSU0O3K6X2ADOBFbqkLDgUQgghRG5aw8o3c0+/zEqH46vBs7qRvJX1NdrtX0vyciZ8Ll7g6Fp4MV5L9u6mYmkFDva2aKBsGXtik9Jl6mBx5tMMRq6HHd/Buv/B1BY8ktyTSJtyvGT3O1XVJaJ0BT7OGMDS2NbWjlaIXMytCCqgEzAMaAb8CvyotT5aMOHdPakICiGEEIUkLQn2/QY7vjeqHyYpGB9r0bCKm6wszUOTN5GemcWqF9tia2O97qWigMVHwV9vwMFFZGnI+UebpB34yG407779rvXiE1v5KD0AACAASURBVKWGxbaP0Ia/tdaPA8OBIcAOpdQGpVRLc64thBBCCCu7ctKoAH5W35huiQbnsqbHFpE1eEXZqoMXOHz+Ks93qCNJYEnjXhUGzCLFoRy3/tGWUWk8mzWXqynp1olNiDyYlQgqpcorpcYppUKBV4DngQrAy8DPBRCfEEIIISwpKwuOroI5/WFyE9j+LdTqaHRMfGYzdPvYWHOXk72zMf1S5ElrzeQ1x/Cr4EKPwCrWDkcUEqe0GJPHK2VFM/nrL4iJjbNwRELkzdw1gtuAn4DeWuucfZlDlVLfmnltIYQQQlhK0hXYMxd2ToeY00bDlnavQ5Mh4J4jcSnia/CKqtWHLnLwXDyfPHI/drZmvQ8vijIPb9Nda5Xizavvk/TFp6TU64LT/X2h9oNGp1shrMTsNYJFpUGMrBEUQggh8uHcv8bav30LICMZqreCZsOhfk+wc7B2dCWC1ppeX20hLjmdtS+3lUSwJMura233z9l/1ZmDq2fTWe3Ek3ije26dzuD/sPG5MJsoiVLDkl1DVymlHtFax2bfuCwwT2vdxczrCiGEEKKwZKTBoSWwYxqEbzd+Ib3/UQgeDpUbWju6Emf9kWj2Rcbxcb9ASQJLuttUzAOA1OptaTdzGw/YH+WDeidxP7UCDi4COyeo3Qn8e0PdLuDkbtWXIUoHcyuCe7TWjW45Fqa1bmx2ZPdIKoJCCCHEHcRHQehM2PWjsc9fuZoQPAIaPQHOntaOrkTSWtN76lYuXU1l/avtsJdEsNTbHxnHkzN2YKMUc4Y1pX7aQTi42Hhz5uo5sHWE2h2NSmHdrvJvU9wTS1YEM5VS1bXWZ7NvXAOTO8cKIYQQwiq0hjNbjOrfoWWgs4yKQ/AIqNUBbCQxKUwbj13i3/BY/tenoSSBAoCAah7MH9WCgdO389j0ncwe1ozAh0Kg60SI2GEkhQcXw5E/wcYearU3ksJ6D0GZctYOX5Qg5lYEuwLTgA3Zh9oAI7XWKwsgtnsiFUEhhBAih9QE2Pursf4v+hA4eUKTJyFoGJTzs3Z0pYLWmn7fbOV8XArrX22Pg50kguKGs5eTeGL6P8QmpTNjaDDN/HIkeVlZELXbmDZ6YDHEnQUbO/BraySF9XuAS3nrBS+KrHupCJqVCGbfrALQAlDANq31JbMumE+SCAohhBDApWNG5889P0NqPFQOhGYjIaCfdCi0sC3HLzFw+nbe6x3A4BY1rB2OKILOxSUzcPp2omKT+W5wEG3rVsw9SGuICsuuFC4yuvoqW/BtbSSFDXqCq5fFYxdFk6UTwbJAHcDp2jGt9UazLpoPkggKIYQotbIy4ehKY/rnyXXGdLL7+kCzEeAdDEo2L7eGAd9t4+zlJDa81g5HO1trhyOKqEsJqQz+YQcnLiYw+fHGdA2onPdgreH8vuxK4SK4cgKUDdQIuZEUut3mfFHiWSwRVEoNB8YB3sAejMrgNq11h3xfNJ8kERRCCFHqJF6GsNmwc4YxdcytKgQPM/b+kwqBVW07cZnHv/+H8T39GRoiU3HF7cUlpTP0xx3sjYjjk0cC6dPY+84naQ0XD95YUxh9GFBQvUV2UtgLPKoVeuyiaLFkIrgPCAb+0Vo3UkrVB97VWj+a74vmkySCQgghSo3I3cbav/2/Q2Yq+D5gVP/qdQdbc/vAiYLw+LR/OBGdwMbX2uNkL9VAcWcJqRmMmBXKP6cu837vAAY2v8fpxBcPG51HDyyCiweMY97NjKTQvxd4VjeO7Z1vcnsLUTJYsmtoitY6RSmFUspRa31YKVXPzGsKIYQQpZupX9Qa9IIDf8DO7yFyF9i7QJPBxt5/Xg2sHbHIYcepK2w7eZm3e/hLEijumqujHTOfCmb03N28+cd+klIzGdGm5t1fwKu+8dH2NWOt8LVK4ao3jY+qTaCsr9GNNCPFOCcuHJaONb6WZLDUMbci+AfwFPAC0AGIAey11g8VTHh3TyqCQgghSoS9841fzNKTbxyzsTP2FktPhPJ1jOYv9z8mm04XUYN/2M6hc/Fseq0Dzg6SCIp7k5aRxYu/7mH5vnOM61iHFzrVQZmzzvfKyRtJYVSY6TEePvDi/vzfQxQZFqsIaq37ZH85Xim1DvAA/jLnmkIIIUSptmbCzUkgQFaG0QDmycVG+3hp/lJk7ToTw6Zjl/i/h+pLEijyxcHOhsmPN8bZwZYv1xwjMTWDN7s3yH8yWK4mtH7R+Bjvicktv+MizIpZFE/5TgSVUjbAXq11AIDWesMdThFCCCFEXrKyIPwfY6qWKRkpULOdJSMS+TB5zTHKuTgwSLaLEGawtVF83C8QFwdbpm8+RWJaJu/3DsDWxsw3gTy8Tf+MkeZSpVK+dzbVWmcB/yqlqt/ruUqprkqpI0qp40qp1008P1QpFa2U2pP9MTy/cQohhBBF1rX9wVa9BV80hJndMLblNcHjLroICqvaEx7LhqPRjHigJmUcpGmPMI+NjWJ8r/t4tl0tftlxlpfn7yEjM8u8i3Z8B+ydbzmoIPEShM0179qi2DH3p1QV4IBSageQeO2g1rpXXicopWyBr4EHgQhgp1Jqidb64C1Df9VajzEzPiGEEKLoiT4K+xcYXT8vHzfWANbuBJ3GQ0YyrHjt5umh9s7GL3CiSJuy5hieZewZ3FKqgaJgKKX4T9f6uDraMWnlEZLSMpnyROP870t5rSFMzmZUrV809iVcPBqidkOXD8HOoeBehCiyzE0E383HOc2A41rrkwBKqXnAw8CtiaAQQghRcsSGG4nf/gXGhtAo8G0NrZ43OoKWKXdjrJ2TtHcvZvZFxLHm8EVe6VwXV0epBoqC9Vz72pRxsOXdpQcZPiuUaYOD8r8GNXBA7p8nTYbA6v/Ctq/g/H4YMBvcKpkfuCjSzG0Wk591gdWAnJOTI4DmJsb1U0q1AY4CL2qtc01oVkqNBEYCVK9+zzNUhRBCiMKVcNHY02v/AgjfbhyrFgRdJ8J9fcCtsunzTP2iJoq0yWuP4e5kx5BWvtYORZRQT4X44eJgx+sL9/LkjO38MDQYdyf7grm4rR10+QCqNobFY+C7NvDoT+DTrGCuL4qkfK8RBFBKXVVKxWd/pCilMpVS8Xc6zcSxW9sXLQV8tdaBwGpglqkLaa2naa2DtNZBFStWvPcXIIQQQhS05FgImwOze8On9WDFq5B6FTq8DWP3wIg10OLZvJNAUewciIrj74MXeLp1TdwK6hdzIUwYEOzDl481JuxsLAO/305MYlrB3qBhfxi+GuydYOZDEDqzYK8vihRzK4JuOR8rpXpjTP28nQjAJ8djbyDqlutezvHwe+AjM8IUQgghCldaEhz9y5j6eWwVZKYZGze3fgkC+kElf2tHKArRV2uP4+Zox9AQX2uHIkqBnvdXpYyDLc/O3c2j07Yx5+nmeLk7FdwNKgfAiHXw+3BY9oKxbrDbJCM5FCWKWRXBW2mtF2FsLH87O4E6Sik/pZQD8BiwJOcApVSVHA97AYcKMk4hhBDCbBlpcOQv45elSbVhwVMQEQrBw2H4WqP61/FtSQJLuMPn41mx/zxPhfji4SzVQGEZHRtUYubQYCJikhnw3TYiYpIK9gZlysHA3+CBl2H3bPjxIYiLLNh7CKszqyKolOqb46ENEITJXSpv0FpnKKXGACsBW2CG1vqAUmoCEKq1XgKMVUr1AjKAK8BQc+IUQgghCkRWJpzZAvsWwKElkBwDzmUh8BGj8lcjBGxkE/HSZMra47g62jGstZ+1QxGlTEjtCvz0dHOGztzBgG+3MXdEC/wquBTcDWxsjUZVVRrBomdhWlt4ZBb4hhTcPYRVKa1vm7fd/mSlck4czgBOA99rrS+aGdc9CwoK0qGhoZa+rRBCiJJOa4jcld3xcyEknAd7F6jf3VhPU7O9tFovpY5duErnLzYyul0tXu1S39rhiFJqf2QcT87YgY1SzBnejPqV3Qv+JhcPw68DIeY0dP4Amo8CZebm9qJQKKV2aa2D7mqsOYlgUSKJoBBCiAJ14eCNvf5iToOtA9TpbFT+6nYFhzLWjlBY2bh5Yfx98AKb/9OBci7yZoCwnuMXrzJw+nZS0rOYPawZ9/t4FvxNUuJg4Sg4ugICH4OeX5jYnF5Y270kguZ2DZ2llPLM8bisUmqGOdcUQgghCt3e+fB5AIz3ND7vnW8cv3IKNn4CU1vCNy1h8xdQriY8PBVeOQaPzYWAvpIECk5EJ7D03ygGt6whSaCwutpebvw2qhVuTnYMnL6d7Scv3/mke+XkAY/9DO3+D/bOgx86Q8yZgr+PsBhzp4aGaa0b3+mYJUhFUAghxF3ZOx+WjoX05BvHbOzBvRrEnjYe+7Qwpn369wZX2Z5I5PbSr3tYsf88m/7TngqujtYORwgAzsUlM3D6dqJik/lucBBt6xbSz68jf8HCkcY6wkdmQs12hXMfcc8sVhEEbJRSZXPcuBxmNqARQgghCtWaCTcngQBZ6RAfCQ9OgBf2w9MrodkISQKFSacvJbJoTySDWlSXJFAUKVU8nJk/qiV+FVwZPmsnf+0/Vzg3qtcVRq4DVy/4qQ9s+dJYTy2KFXMTwU+BrUqp97K7fm4FPjY/LCGEEKIQpF6FuHDTz2VlQMg48PQx/bwQ2b5edxx7WxtGtKlp7VCEyKWCqyPzRrQgoJoHz/0cxh9hEYVzo/K1YPgaaNAT/n7H2EInLbFw7iUKhVmJoNZ6NtAPuABEA3211j8VRGBCCCFEgUmIhjXvwef35T3Gw9ty8Yhi6+zlJBaGRfJE8+p4uckG26Jo8ihjz09PN6eZbzlemv8vc/4ppLV8jq7GlhKdxsPBxTC9E1w+UTj3EgXO3GYxLYBwrfVXWuspQLhSqnnBhCaEEEKY6copWPYSfBEAmz4FvzZGo4NbO93ZOxv7ZQlxB1PXH8fWRvFM21rWDkWI23J1tGPmU8G0r+fFW4v2M21jISVoSkHrF2HgAoiPgu/bw7G/8325RWGRhExci9/rywmZuJZFYbKRfWExd2roN0BCjseJ2ceEEEII6zm3FxYMgylNIOwnCBwAY3bCo3Og3X+g52Tw8AGU8bnnZGOMELcREZPEgl0RPB7sQyV3qQaKos/J3pZvBzWle8Mq/O/Pw3z291EKbeu42h1h5HrwqA5zH4GNkyAr654usSgskjcW7iUyNhkNRMYm88bCfZIMFhJzG7soneNvk9Y6SyklzWKEEEJYntZwehNs/hxOrAUHN2g5BlqMBvcqN48NHCCJn7hn36w/gY1SPNNOqoGi+HCws2Hy441xdrBl8ppj/Bsew/GLCUTFplDV05lXu9Sjd+NqBXOzcn7w9CoyFz+P7dr3STi1k2OtJhGb6Ux8SjpxyenEJ1/7nHHjWIrxODwmKVfPmeT0TCatPFJwMYrrzE3aTiqlxnKjCjgaOGnmNYUQQoi7l5UJh5cZe/5F7QYXL+j4XwgaBs6FsKmyKJWiYpOZHxrOgCAfqnjIJtqieLG1UXzcL5ALcclsOHrp+vFrFTfgpkQrIzOLqykZNyVpN75ON3k8Z3KXltGHYbbO/N/Jubid6MrL6S9xUle9fn0nexvcnezxcLbH3dmeiq6O1K7oytkrSSbjj4pNNnlcmMfcRPAZYDLwFqCBNcBIc4MSQggh7igjFf79BbZOgcvHoawf9Pgc7n8C7GXanihY324w1leNbl/bypEIkT82NoqTl3J39UxOz+S1BXv5dsMJ4pPTiU/JICE147bXsrVRRhLnZIe7s5HQVfVwxt3ZeOzuZI+7c0O2J3QieOeL/O04noudJmPn3wN3Zzsc7WxNXnfn6RgiTSR9lT3kZ3phMCsR1FpfBB4roFiEEEKIO0uJh9AZ8M83kHAeqtwP/WeC/8PG5sZCFLDzcSnM2xFO/6beVPOUaqAovqJiU0weT8vMwqdcmezkzh53Z7scX1+r3Nldr+KVcbBFKXUXd6wBwUHw6yCqrBgGia9BuzfyHP1ql3q8sXAfyemZNx33dLYnM0tja3M39xR3y6xEUCnlBDwN3AdcT9W11sPMjEsIIYS42dULsP0b2PkDpMZDzXbQ51vj8139QiJE/ny38QSZWjO6nVQDRfFW1dPZZMWtmqcz3z8ZVDg39fCGp/6C5S/Dxo/h3B7o+73JqfvXpqdOWnmEqNhkqno606p2eX4LjeB/fx7i7R7+hRNjKWXu1NCfgMNAF2ACMBA4ZG5QQgghxHWXT8DWybDnF8hKhwa9oPULULWxtSMTpcDF+BR+3n6Wvo2r4VOujLXDEcIspipuzva2vNqlXuHe2N4JHv4KqjWGFf8xtph4dC5Uyp3Y9W5cLVdjGBcHO37YfIraXq483qx64cZaipibCNbWWj+ilHpYaz1LKfUzsLIgAhNCCFHKRYUZDWAOLgZbB2j0BLR6HspLx0ZhOdM2niQjSzOmg1QDRfFnquJWoF1Db0cpCB4OXvfBb0OMzed7fw339bnjqW91b8DJS4m8vWg/vuVdaFmrfOHHWwooc/YSUUrt0Fo3U0ptxOgYeh7YobWuWVAB3q2goCAdGhpq6dsKIYQoSFrDyXVGAnhqAzi6Q/DT0PxZcKtk7ehEKXMpIZXWH63loYZV+GxAI2uHI0TJEX8O5j8JETsgZJzR6fkOa7zjU9LpO3UrlxJSWTQ6BN8KLhYKtnhRSu3SWt/VPF9zN5SfppQqi9E1dAlwEPjIzGsKIYQobbIyYf9CmNYWfuoD0UfgwQnw4gHoNF6SQGEV3286SVpGFs9Jp1AhCpZ7FRi63NjmZ8uXMKcvJF25/SlO9vwwxMhvnp61k7jkdEtEWqKZVREsSqQiKIQQxVB6CuyZa2wBEXMKyteGVmPh/sfAztHa0YlS7EpiGq0/WsuD/pX48jFZjypEodk922gk41YZHp1jdIK+jX9OXmbQ9O20rFWemUODsbM1t65VsliyIiiEEELcu+RY2PQpfNEQlr8EzmVhwE/w3A5oOkSSQGF10zedJDk9k+dlbaAQhavJk0ZX0cwM+KEzLHsJPg+A8Z7G573zbxreomZ5PugTwKZjl3h/ufSoNIe5zWKEEEKIuxd/Dv75GkJ/hLSrUKuj0QHU9wHZAkIUGbFJaczaepruDatQ28vN2uEIUfJ5N4VRG2DmQxD6w43jceGwdKzxdeCA64cfDa7OsQsJTN98ilpergxuUcPCAZcMkggKIYQoeHvnw5oJEBdh7CHVbCRcOgp7f4WsDKNLXMi4O04BEsIaZmw+RWJaJs93qGPtUIQoPVy9ICP3HoekJxv/n+RIBAHeeKgBJ6ITGL/kAH7lXWhdp4KFAi05zN1Qvq+Jw3HAPq31RXOuLYQQopjaO994Bzc9+z/0uHD4+21QdhA0FFqOgXJ+Vg1RiLzEJaUzc8tpugVUpl5lqQYKYVFxkXkcj8h1yNZGMfnxxvSdupXRc3ex6LkQalZ0LeQASxZz1wg+DUzH2Eh+IPA98BKwRSk12MxrCyGEKG7Sk2Hl/91IAnNy84Lun0oSKIq0mVtPcTU1Q6qBQliDh/c9HXdzsueHIUbDmOGzQolLkk6i98LcRDALaKC17qe17gf4A6lAc+A/5gYnhBCiiMtIgzNbYf1HMLM7TKwOidGmx8afs2xsQtyj+JR0Zmw+RWf/SvhXdbd2OEKUPh3fAXvn3MdrtsvzlOrly/DtoKaExyQx+uddpGdmFVp4JY25iaCv1vpCjscXgbpa6yuApORCCFHSZGZARChs+gxm94aPasDMbrD+Q6P5S7ORUCaPdRp5vdMrRBExe+tp4lMyGNtRqoFCWEXgAOg5GTx8AGX8v+F1H/z7C5xcn+dpzfzK8b8+Ddly/DITlh60WLjFnbnNYjYppZYBv2U/7gdsVEq5ALFmXlsIIYS1ZWXBhX1wahOc2mhU/9KuGs95+UPjweDXBmq0gjLljONV7r95jSAY7/B2fMfy8QtxlxJSM5i++RQd63sRUM3D2uEIUXoFDri5MUxKPMzoAr8+CcNXQ8W6Jk97JMiH4xcT+G7jSepUcuXJlr6WibcYMzcRfA4j+QsBFDAb+F0bu9S3N/PaQgghLE1riD6cnfhtgDNbIDnGeK58bWjY30j8fB8A14qmr3HtP/CcXUM7vpOr45sQRcnsbaeJTUqXaqAQRY2TOzzxK3zfAX5+BIavBZfyJoe+1rU+J6ITeHfpQXzLu9Cmbh7/TwkAlJGzFX9BQUE6NDTU2mEIIUTxojVcOWlU+05thNObbqzx86wOvm2MxM/vAXCvat1YhSgkiakZPPDxOgK9PfjxqWbWDkcIYUr4TvixO1RrAk8uBjtHk8MSUjPo/81WImOT+WN0CLW9SlcnUaXULq110N2MLYjtIz4CvDAqggrQWuvbrrBWSnUFvgRsgela64l5jOuPMe00WGstWZ4QQhSE2LM3pnqe3gTx2e263apArQ5Gtc/vASjra9UwhbCUudvPcCUxTTqFClGU+QRDn29gwTBY8jz0+Q6UyjXM1dGO6UOC6P31Fp6etZNFo0Mo6+JghYCLPnOnhn4M9NRaH7rbE5RStsDXwINABLBTKbVEa33wlnFuwFhgu5kxCiFE6RZ/zkj4riV+MaeN42UqGAmf7wPg1xbK1zL5n6oQJVlyWibTNp7kgToVaFqjrLXDEULcTkA/uHwS1r0P5etA21dNDvMuW4bvBjfl8WnbeXbuLmYPa46Dnbk9MksecxPBC/eSBGZrBhzXWp8EUErNAx4Gbm3x8x5GovmKmTEKIUTJs3d+3mvwEi/fSPxObYTLx4zjTh5G0tf8WWO6p1cDSfxEqTd3+xkuJaQxTtYGClE8tHkFLh/PTgZrGsmhCU1rlGNiv4a8NP9f/rtkP//r0xAl/+fdxNxEMFQp9SuwCGP/QAC01gtvc041IDzH4wiMfQevU0o1Bny01suUUnkmgkqpkcBIgOrVq9979EIIURztnX9zV864cFj8HPw7DxIuwIX9xnEHV6ObZ5MnjcSvckOwsbVe3EIUMSnpmXy38SStapUnyLectcMRQtwNpaDXZIg9A388Cx7VjWmjJvRt4s3xiwlMXX+COl5uDGvtZ+FgizZzE0F3IAnonOOYBm6XCJpKxa93rFFK2QCfA0PvdHOt9TRgGhjNYu4crhBCFCNZWZAaB0lXIDkWkq8YX6947eatGQAy0+DEWqjZ1qgO+raBqo3A1t46sQtRDMzbcZboq6lMebyxtUMRQtwLO0d4dC5M7wDzHofha6BsDZNDX+lcj+MXE3h/+UH8KrrQvp6XhYMtusxKBLXWT+XjtAjAJ8djbyAqx2M3IABYn12+rQwsUUr1koYxQgiLud3Uy3ulNaQnZSd0V4ztGG76Oib38aQrkBILOuve7vXk4vzFKEQpk5KeyTcbTtDMrxwtappuRS+EKMJcysMTv8H0TvDLYzBspbHVxC1sbBSfP9qIR77dxvM/h7FwdCvqVnKzQsBFT74SQaXUa1rrj5VSU8hRzbtGaz32NqfvBOoopfyASOAx4Ikc58YBFXLcaz3wiiSBQgiLMTX1cmn2jzX/3kbCZjKhy5HE5azgJcdAZmre97N3MTZjd/YE53Lg0dD47Fw2+/gtX8/udaPTZ04e3gX/vRCihPotNJwL8al8PqCRtUMRQuRXxbrw6GyY0w8WPAWP/wq2udMbl+xOor2+MjqJLn6uNeWkk2i+K4LXGsTcc3Kmtc5QSo0BVmJsHzFDa31AKTUBCNVaL8lnTEIIUTDWTMg99TI9GRaOhIUj8j7Pxv7mxK1cTajW9OYkrkz2czm/zmMvpDx1Gn9zogpg72xULYUQd5SakcnU9ScIqlGWlrWkGihEsVazHXT/FJaOg79eh+6fmBxW1dOZ759syqPT/uGZn3YxZ7h0Es1XIqi1Xpr9eVY+z/8T+POWYyZ/g9Fat8vPPYQQ4p6lJcH+BUYF0CQN7d/MkdjdktA5uFqmC+e1KaoFNXVViFLm912RnItL4aN+gdJFUIiSoOlQuHQMtn0FFepA81EmhzWuXpZJ/QMZN28Pb/6xj4/7l+6fAeZuKF8XY3sH35zX0lp3MC8sIYqBglxDJqwr+iiEzoA9PxvNWWzsICsj9zgPH2j7muXjMyVwgPx9EyIf0jOz+HrdcRr5ePJAnQp3PkEIUTw8OAGunDKqgmX9oG5nk8MeblSNExcTmLz2OHUruTGiTU0LB1p0mNs19DfgW2A6kGl+OEIUE7dbQya/nBcPmelweBns/MHYc8/GHvx7QdDTxvo7mXopRIm0cHcEkbHJvN8noFRXAoQocWxsod/3MKOrsV5w2EqoHGBy6Aud6nI8OoH/rThEzYoudGxQycLBFg1K6/zvuqCU2qW1blqA8eRbUFCQDg2VfjLCQj4PMD190MMHXtxv+XjE3YuLhF0/wu7ZkHDe2H8oaCg0HgyuOVpKS8VXiBInPTOLDp+up2wZBxY/FyKJoBAlUXwUfN8BlC2MWAtuppO85LRMHvluK6eiE1k4OoR6lUtGJ9Hs/CzorsaamQiOBy4Cf3DzhvJX8n3RfJJEUFhMViZMyGvjYQXjYy0ajrgLWVlwcp1R/Tu6wtjOoc6DRvWvzoOyyboQpcSCXRG88tu/TH8yiE7+pbMCIESpELUHZnYDrwYwdLkxq8eEc3HJPPzVFhzsbFj0XAgVXO+xeVsRdC+JoLmtcoYArwJbgV3ZH5KNiZIpMwP+nQdTW+Q9xq2K5eIRd5Z0BbZMhilNYE5fCN8OIeNg3B4Y+BvU6ypJoBClREb22sD7qrrTsYFsKC1EiVa1EfT9HiJ3wx/PGG8Im1DFw5nvnwwi+moqz/y0i9SM0rXSzaxEUGvtZ+Kj9K64FCVTRqoxlfCrpvDHKGMtWbORpt9dSroM26YaSaOwDq0hfCcsHAWf1oe/3wa3ytDvB3jpoLH1QllfKwcphLC0pXujOHUpkbEd68iUUCFKgwY9jAYyBxfBug/yHHa/jyefDrif0DMxB6sKRgAAIABJREFUvLFwH+bMlixu8ruhfAet9VqlVF9Tz2utF5oXlhBFQHqysY5sy5dG85CqTaDLh1C3K9jYgHfwzWvIWjwDJ9bByjeM7pM9PgOfZtZ+FaVHagLs+w1Cf4Dz+8DBDZoMhqBhUOk+a0cnhLCizCzNlLXHqV/ZjQdLaVMIIUqlVs/D5WOw6RMoXwsaPWFyWI/Aqhy/mMAXq49Rt5Ibz7StZeFArSO/XUPbAmuBniae04AkgqL4Sr1qrCXb9hUkRkP1VtBrCtTqcPMecaba97d4Dg4tgRWvww8PQpMnodO7xj5zonBcPGwkf//Og9R4qBQA3T8z/mwcS8bCbyGEeZbvO8fJ6ESmDmyCjY1UA4UoNZQyfieIOQ1LxoJnDfANMTl0XMc6HL+YwEd/HaZmBRc631fZsrFagVnNYooSaRYjzJYcA9unwT9TISUWaraHNq/m+QPjtlKvwvqJ8M834OxpTE24/wmjkijMl5FmJNyhM+DMFrB1gPv6GM1ffJpZZlN3IUSxkJWl6fLFRpSCv8a1kURQiNIoOQamPwhJl2D4GqM6aEJKeiaPfreNYxcTWPBMK/yruls4UPNZrGto9s26A/cBTteOaa0nmHXRfJBEUORb4iXY9jXs+B7SrkLdbtDmFfC+q39Dt3d+Pyx/yWhSUr2l8a5UJX/zr1taxZ69sfVDYrTxzl7QMGg8CFxkY2ghRG7L957juZ93M+XxxvS8v6q1wxFCWMuVk/B9R2OW1vDV4FzW5LAL8Sk8/NUWbBQsGhOCl5uTyXFFlSW3j/gWKAO0x9hUvj+wQ2v9dL4vmk+SCIp7Fn8Otk6BXTON9YD39YYHXobKDQv2PllZsGcu/P0OpMRBy9HQ9nVwdC3Y+5RUWZlwfI0x/fPYKuNY3a5G9a9WB6myCiHylJWleWjyJtIzs1j1YltspRooROl2ZivM6gXVW8CghWDnYHLY/sg4+n+7lQZV3PllRAuc7ItPh/H/Z+/Ow6Oqrz+Ov09CIGENyFJ2UFllFXDDumEFRdG671pr3YrWtmq1/bnbSkVrtdVWrRat+1ZlUVHcqaiAyL6I7IRNIEAggSzn98e9gRAmZJvMTJLP63nykLlz73fO3LkT5sx3ObFMBGe5e58i/zYE3nT3kyrcaAUpEZQy27w8WABmxn+CJKPPuXD0b6BF16p93O0bYdKdweM2bgvDRkGP0zSMsSTbfwjO1bR/Q+ZyaNAymHM54HJIbx/v6EQkgb01YzWjJy5kdWY2ABcf0YH7zojyl3wiUj19+xK8dQ30vyRYA6KEz2Hvzl7DtS98wxn92vDwef2qzWrD5UkEK7pYTKGc8N8dZtYG2Ah0rmSbIlVj4/fw+V9g1suAQf+LYPCN0CxGl2yDA+D0vwd/eCb8Bl69BLqcBCc/ELsYEp17MIx26tPBcs/5u6DTj4OSD91PLfGbOxGRQm/NWM1tb84mO3dPPbDXp69iYMdmnNG/bRwjE5GE0O8C2PQ9fDYamncJ6gtHcHLv1tx0UlcefH8RXVo14pfHHxzjQKteZRPBcWaWDowGviFYMfSpSkclEk3r5sHnD8HcN4NFRQZdCUfdAE3i9IGgw+Fw1afw1T/hk/uDAvU/vgkG3wB16sUnpniY9eqe8huN28CBx0LGTFg/F+o1hgE/C+b/tewe70hFpBoZPXHhXkkgQE5uAaMnLlQiKCKB434PGxfDB3dCswODEVoR/PL4g/lufRajJy7koBYNGNardYwDrVoVHhpqZknAEe7+RXi7HpDq7luiGF+ZaWio7CNjBnz2ICwYD3UbwqCfw5EjoWHLeEe2x5bVQd3BeW/DAV1g+INw4HHxjqrqzXoVxt0QzM0sqnF7OPZm6H021G0Qn9hEpNrZkp3LNys2883yzfzto8UR9zFg6ajhsQ1MRBJXbjaMORXWzYUr3oU2/SPulpObz/lPfsnCtdt47Zoj6dW2SYwDLZ9YzhGc4u5HVriBKFIiWAFFe2SatIMhd+xbF686WvFl0N2/eBKkNoHDrwl+ErmW33eT4J3fBnVuep0NQ/8EjWpg0eOC/OD1efG8YIXW4pq0h1/PiX1cIlJtuDvLNu5g+vLNTF++ienLN7NoXRYAyUlGkkFu/r6fbdqmp/G/W0+Idbgiksiy1sNTJ0B+LvzioxJHi63flsMZf/8fBQ5jRw6mZePEXUk0long3cAsggVi4lqQUIlgOUXqkUlJg9MerZ7JoDss/SxIAJd9DvUPgCN/GQwDTU3sb252y82GyQ8HP3VS4YTbg17MpOqzUlVEu3bA9x/Bwndg0XuwY+N+dja4KzNmoYlI4svJzWfO6i1MX76ZacuDXr+N23cB0Ci1DgM6NmVAh6YM6NiUvu3T+WDeun3mCKalJHP/mb01NFRE9rVuLjw9FJp1gp+9V+Kq7vMytnL2P7+gS6tGvHJV4q4kGstEcBvQAMgjWDjGAHf3mFdfVCJYDnk74S89In8gr9cYTroPmnYMarQ1aQfJKbGPsazcg5ICnz0Iq76Ghj8KJv0OuKz6Di38YXHQO7jkE2jdF059GNoOiHdU5bP9hyDpWzABvv8Y8rKDhLzLUOg+HCb+Hrau3vc49QiK1Hobtu3cq7dvzuqt7MovAKDTAfUZ0LEZAzo2ZWCnphzcomHEAvGFq4ZmZGbTJj2Nm4d2UxIoIiX7bhK8eE7wOeX8F0r8En7i3LVc/Z/ppKUkk5Obn5B/X2JaUD5RKBEshTusmgozX4I5b0JOGXtdLCkodZDecU9ymN5hz++NWsenjltBASwYFySAa2dBkw5w9I3Q7yJISdzu+jJzDxa3ee/3kLUuWDRlyO0lFj9NCBu/D3r9FrwDK78EL4DG7YLEr/sp0HHwni8ValqPtIhUSH6B8936bUHit2wz01dsZvnGHQDUTU6iT7smQY9fx6Yc2rEpzRvWogW1RCS2vnoS3r05WE9i6B8j7vLWjNXc9NpM8gr25E+JNuIglj2CH7r7kNK2xYISwRJsWhp86J71MmxaAnXSoMepQS/Njh/23b9JO7j8naBu2+blwb+ZK/b8vm3N3vsn1w2O2SdR7BT83qB5dOvk5ecFCdLnD8GGBdDsoKAIfJ9zE7vnsqJytsLHf4KvnwiGu550H/Q5LzFqD7pDxjdB4rdgAmyYH2xv1StM/obDj/qUHGtNnaMqIiXK2pnHzJWZTAuTvhnLN7NtZx4AzRvW3Z30DejYjF5tG1OvTmIOvRKRGuqdm+HrJ+HUv8LAn+1z9+BRH+2uT1pUIs1BrvI6gmaWCtQHmptZU4IhoQCNgTYVaVOiKDsT5v4XZr0CK6YABp2ODkoU9DgNUhuX3CMz5M4goWvaMXJFyNyc4IN75rIwOVyxJ2mcP27f4aYp9YPEcHei2GHvpDEtveTnsVei0BYOOgGWfg6bl0LLnnDW03DIT6v/HLr9SW0MJ48Kat6M/w3892r45j8w/KH4lFXI2xXMwVwwARa+C9sygl7jjoPh0PuDnr+mncrWVp9zlfiJ1GDuzurM7HCYZ/Azf81WCjz4fqhbq0ac1q8NA8Pkr0Oz+tWmYLOI1FBD7w86USb8Nvg8c9Dxe92dESEJ3N/2RFehHkEz+xVwI0HSt5o9ieBW4Cl3/3vUIiyjWt8jmJ8brJI58+XgA3r+TmjeDfqeB73PhfT2+x5TFT0yO7P2Tg73+n057Ny69/71mkDTwuSw055EccN8+OTPwdyyotI7wrD7oevJ8RmSGk8FBfDNszDpLtiVBUddD8fcAnXrV+3j5myB7z4Ihn1+90HwGqbUDxLz7qdC16GJvSKriERdpDl4w/u0Zl7G1t0Lukxbvol1W3cCUL9uMv07pAeLunRqRr/26TRJq4GjOESk+svZCs8MDUp8XfkBtOi2+66a1iNY2aGh17v73yrcQBTVykTQPaiVN/NlmPNGMNSz/gHQ+5xg+GCb/okxhLCQezA3MdKQ08KksXjiV5wWE4GsDTDpTvj2hWBu5CkPQLeTo/sYWzPC+X4Tgl7Yglyo3zx4nO6nBsXfU9Ki+5giUi28NWP1PqtyJlnwkxes6ULb9LTdC7oc2qEp3X/UiDrJtezLOxGpvjJXwFNDgs86v/gomOpE5L9/tXaOYCKpVYlg5kqY/WqQAP6wCJLrBR/Q+14ABw+pvnPl3GH7hiApfPrEEnZSeYHdln8RDBfdMB+6nQIn/znoUa0I92DO5YLxwZy/jG+C7c0ODOf7nQrtBtXsYbgiUqrsXfkcNepDNu/I3ee+BvWSeeCsvgzo2JQfNakBi3aJSO22ahqMGQ6t+8FlY6FOsFhVoq9KrESwJsrZCvPHBsnfssmAQ4ejgqGfPc/Y/1y76ujhXrBl5b7b1SO4t/xc+PJx+GRUkMwde0uw2lWduqUfW5APK78Kev0WTAjmXkJQqqL7cOg2PBgOkUi9yiISc7vyCpi8eANjv83gg3nr2L4rP+J+BiwdNTy2wYmIVKU5b8LrPwumWZ35ZLX4TFTli8VIjOTnBbXkZr0M88cHwyabHQjH/z6Yy1fWRTmqoyF3lLCYzR3xiykRJacEdRMPORPeuxU+vDtYJGj4Q8HwzuJzQLufGlxTCybAoneDxX2SUoKhnkddH/QsNm4d72clInGWX+B8tXQj42au4d05a8jckUuTtBRG9GvDB/PW8UPWrn2OaZOu4eIiUsP0OhM2fQ8f3QcHHAzH/S7eEUVVpRLBRCofUaOsnR30/M1+Laghl5oO/S4Mhn62G1gtvo2otMJFa1ReoGzS2wcFUBe+F9TAGTMcLBk8/OZ+y0r47zVAEnhusEhPl58EPX8HnxisTioitZq7M3PVFsZ+m8GE2Rms27qT+nWT+UnPVozo24Yfd2lB3TpJHN458hyZm4d220/rIiLV1I9vCmolf/InOOAg6H12vCOKmriUjzCzYcAjQDLwL3cfVez+a4BfAvlAFnCVu8+rSKzVxtY1QeI382VYPzfopek6FPqeD11O2j0uuVZReYHy6zYMOh8DD3WFndv2vs/zoW4anPcqdDy6bMNHRaTGW7RuG2O/zWDcrAyWb9xB3eQkjuvWghH92jCkeyvS6u49N7hwLkwiz5EREYkaMzjtkWANi7euC9ZjaH9YvKOKipiXjzCzZGAR8BNgFTAVuKBoomdmjd19a/j7COA6dx+2v5iq5RzBXduDIZ+zXg6G63lBsCBHn/Og11lakl8q7q50INJ7W4vtiAis2LiDcbMyGDczgwVrt5FkMPjg5pzWtw1DD/mRSjuIiBS3fSP8a0iwentqI9i2NiFHrFX5HEF3fwR4pILlIw4DFrv7EgAzexk4HdidCBYmgaEGRP5Em9hKqtFXkB8U5J75MswbC7nbg28WfnxTkAA2PzjekUtN0KRdCYvttIt9LCKSENZvzWH8rDWMnZnBtyuDL4QGdGzK3SMO4ZTerWnRqBaOPBERKasGB8DAn8EHd0BuVrBty8pgTQtIqGSwrCq7WMxaM2vk7tvM7P+AQ4H73P2b/RzTFij6CXUVcHjxnczsl8BvgLpAYlRoLKtZr+690MmWlTD2epj9BqybDVtXB3O0ep8VzPtrf0TtK44uVUuL7YgIkLljF+/NWcvYmRl8uWQjBQ49Wzfm1pO7c2qf1rRrWj/eIYqIVB9fP7XvttzsoPOnFiaCt7v7a2Z2NDAUeBD4BxESuyIirXSyT4+fuz8GPGZmFwL/B1y2T0NmVwFXAXToUMH6aVXhw3v2/gAOkJcD370HXYfBSfcFdf9UkFuqihbbEam1tu/MY9L8dYz9NoPPvttAbr7TuXkDRp7QhRF923Bwy4bxDlFEpHrasqp82xNcZRPBwiXDhgP/cPe3zeyuUo5ZBbQvcrsdkLGf/V8mSC734e5PAk9CMEewLAHHRIkXg8GFr8Q0FKnFtNiOSK2xMy+fTxduYOzMDD6cv57s3HxaN0nlZ4M7c1qfNvRq2xirDStOi4hUpRo29aayieBqM3sCOBH4s5nVA0ob4zgV6GJmnQkWmjkfuLDoDmbWxd2/C28OB76jOqlhF4mIiCSevPwCpizZyNhvM3hv7lq25eTRrEFdzhrQlhF92zKwY1OSkpT8iYhETQ2belPZRPBcYBjwoLtnmllr4Ob9HeDueWY2EphIUD7iGXefa2b3ANPcfSww0sxOBHKBzUQYFprQathFIiIisfXWjNURyzO4O9+s2BzW+lvDD1m7aFivDicdEtT6G3xwc1KSNedcRKRK1LCpNxUqH7FXA8H8wC7u/m8zawE0dPelUYmuHBKufERJq4aKiIjsx1sz9i3YXq9OEkcf3JwFa7exOjObenWSGNKjJSP6tuG4bi1JTUneT4siIlJblKd8RKUSQTO7ExgIdHP3rmbWBnjN3QdXuNEKSrhEUEREpAIGj/qI1ZnZEe87rlsLRvRtw096tqJRqmr9iYjI3qq8jmARPwX6A98AuHuGmTWqZJsiIiK1VkYJSaABY352WGyDERGRGquyEwl2edCl6ABm1qDyIYmIiNRO36zYTHIJC7y0SVfJIRERiZ7K9gi+Gq4amm5mvwCuACJUWhQREZGSbM3JZfR7C3n+q+U0Sa3Djl0F7Mov2H1/WkoyNw/tFscIRUSkpqlUIujuD5rZT4CtQDfgDnf/ICqRiYiI1HDuzsS5a7lz7Fw2bNvJ5Ud14rcndWPSvHURVw0VERGJlkqvGrq7IbPmwEaPVoPlpMViRESkOsnIzOaOt+cyaf46erZuzKizetOnXXq8wxIRkWqsyheLMbMjgFHAJuBe4D9AcyDJzC519/cq0q6IiEhNl1/gPDdlGQ9OXEiBw+9P6c4VgztTR/X/REQkhio6NPTvwO+BJsBHwMnu/qWZdQdeApQIioiIFDM3Ywu/f3M2M1dt4diuLbjvjF60b1Y/3mGJiEgtVNFEsI67vw9gZve4+5cA7r7ALPJqZyIiIrXVjl15PDLpO/41eSlN66fw6AX9Oa1Pa/R/poiIxEtFE8GCIr8XL3gUlzmCIiIiieiThev5v7fmsGpzNhcc1p5bh/WgSX0VgxcRkfiqaCLY18y2EtS3TQt/J7ydGpXIREREqrEN23Zy7/h5jJ2ZwUEtGvDq1UdyWOdm8Q5LREQEqGAi6O7J0Q5ERESkJigocF6dtpL7311A9q58bjyxC9cedxD16ui/ThERSRyVLSgvIiIiocXrs/j9f2fz9dJNHNa5GX/6aW8Obtkw3mGJiIjsQ4mgiIhIJe3My+fxj7/nH598T1rdZP58Vm/OGdCepCQtBiMiIolJiaCIiEglfLVkI7//72y+37Cd0/u14fZTe9K8Yb14hyUiIrJfSgRFREQqYMuOXO5/dz4vT11J+2ZpPHvFYRzbtUW8wxIRESkTJYIiIiLl4O6MnZnBvePnsXlHLlcfeyA3DulKWl0tBiMiItWHEkEREZEyWrlpB394aw6fLdpA33ZNePaKwzikTZN4hyUiIlJuSgRFRERKkZdfwNOTl/LwpEUkm3HnaT259MhOJGsxGBERqaaUCIqIiOzHzJWZ3PbmbOat2cpPerbi7hGH0CY9Ld5hiYiIVIoSQRERkQiydubx4MSFPDdlGS0a1eOfFw9gWK8fxTssERGRqFAiKCIiUswH89Zxx9tzWLs1h0uO6MhNQ7vRODUl3mGJiIhEjRJBERGR0NotOdw1di7vzV1Lt1aNeOyiQzm0Q9N4hyUiIhJ1SgRFRKTWKyhwXvhqOX9+byG5+QXcMqwbv/jxgaQkJ8U7NBERkSqhRFBERGqdt2asZvTEhWRkZtOiUT3SUpJYvimbow9uzh9/2ouOBzSId4giIiJVSomgiIjUKm/NWM1tb84mOzcfgPXbdgJw0RHtue/03pipJISIiNR8SgRFRKRGKShwNu/YxfptO1m3NYf1W3eyflsO67YGtz9euJ7cfN/nuE8W/ICdoSRQRERqByWCIiJSLbg7mTtyWRcmdeu35uxO9taFvxcmfZESvfT6KbRsVC/ifQAZmdlV/RREREQSRlwSQTMbBjwCJAP/cvdRxe7/DXAlkAdsAK5w9+UxD1RERCqk6By8Nulp3Dy0G2f0bxtxX3dnS3ZukNwV6bnbO9HbyYZtO9mVX7DP8U3SggSvVeNUDj+wAS0bpdKqcXC7VeN6tGyUSotG9UhNSQZg8KiPWB0h6VOReBERqU1ingiaWTLwGPATYBUw1czGuvu8IrvNAAa6+w4zuxZ4ADgv1rGKiEj5FZ+Dtzozm1ten8XXSzfSrln9vYZqFv67K2/fBK9Rap3dydzhnZvRonE9WjVKpVXjVFqGv7dsvCfBK6ubh3bbKz6AtJRkbh7arXJPXEREpBqJR4/gYcBid18CYGYvA6cDuxNBd/+4yP5fAhfHNEIREamw0RMX7pVkAezKL+DFr1cC0KhenSCRa5zKgA5Nw8RuT+9d4b9pdcuX4JVVYc9kWXssRUREaqJ4JIJtgZVFbq8CDt/P/j8H3o10h5ldBVwF0KFDh2jFJyIilRBp2CWAAXPvGUr9uvGfnn5G/7ZK/EREpFaLR6XcSEuyRZy5b2YXAwOB0ZHud/cn3X2guw9s0aJFFEMUEZHycnce/2Rxife3SU9LiCRQRERE4tMjuApoX+R2OyCj+E5mdiLwB+BYd98Zo9hERKQCsnflc/PrMxk/aw2Htk9n3tqt5OTumfenOXgiIiKJJR49glOBLmbW2czqAucDY4vuYGb9gSeAEe6+Pg4xiohIGa3OzObsf37BhNlruPXk7rxx3VGMOrMPbdPTMKBtehr3n9lbQzFFREQSSMx7BN09z8xGAhMJykc84+5zzeweYJq7jyUYCtoQeM3MAFa4+4hYxyoiIvv31ZKNXPfCN+zKL+CZywZxfPeWgObgiYiIJLq4TNZw93eAd4ptu6PI7yfGPCgRiany1JmTxPT8l8u5a+xcOhxQn6cuHchBLRrGOyQREREpI83aF5GYi1Rn7rY3ZwMoGawGduUVcNe4ubz41QqO79aCRy7oT+PUlHiHJSIiIuUQjzmCIlLLRaozl52bz+iJC+MUkZTVD1k7uehfX/LiVyu49riD+Ndlg5QEioiIVEPqERSRmHL3EuvMZZSwXRLDnNVbuOq5aWzasYtHzu/H6f3UeysiIlJdKREUkZhZsHYrt781p8T7G6fVIS+/gDrJGqyQaMbOzOCW12fSrH5dXr/mKHq1bRLvkERERKQS9GlLRKrctpxc7hk3j+GPTmbx+izOG9SOtJS9//wkGWzJzuPUv03m66Wb4hSpFJdf4Pz5vQXc8NIMerdtwtjrj1YSKCIiUgOoR1BEqoy7M3ZmBvdNmM8PWTu54LAO3DK0G+n163Lkgc33WjX0ppO6Ur9eHe4ZN49zn5jCmf3bctspPWjRqF68n0attTUnl1+9NIOPF27gwsM7cNdph1C3jr4/FBERqQnM3eMdQ1QMHDjQp02bFu8wRCS0aN027nh7Dl8u2USfdk249/Re9G2fXupxO3bl8djHi3nysyWk1knmtyd15eIjOmq4aIx9vyGLXzw3jRUbd3DXiEO4+IiO8Q5JRERESmFm0919YJn2VSIoItGUtTOPRz/8jmcmL6VBvTrcMqwb5w/qQHKSlaudJRuyuHPsXD7/7gd6tm7MvWccwoCOzaooainq44XrueGlGaQkJ/GPiw7l8AMPiHdIIiIiUgZKBEUk5tydCbPXcN/4+azdmsN5A9vzu5O706xB3Uq1+e6ctdw7fh5rtuRwzoB2/O7k7jRvqOGiVcHdeeKzJfz5vQX0+FFjnrx0AO2a1o93WCIiIlJG5UkENUdQRCpt8fos7hw7h/8t3sghbRrz+MWHcmiHppVu18w4pXdrju3agr99tJh/fb6EiXPXcvOw7lx4WPl7GaVk2bvy+d0bsxg7M4PhfVoz+uw+1K+r/yJERERqKvUIikiF7diVtztBS01J5uah3bjo8I5VlqAtXr+N29+ay5QlG+nVtjH3nt6L/lFIOGu71ZnZXP2faczN2MpNJ3XjuuMOwkxJtoiISHWjoaEiUqXcnffCIZsZW3I4e0A7bo3RkE13Z9ysNdw3fh4bsnZy/qD23Dy0ckNQa7OpyzZx7fPTyckt4JHz+zGkR6t4hyQiIiIVpKGhIlJllv6wnTvHzuWzRRvo/qNGPHpBfwZ2it0iLmbGiL5tOKF7Sx6ZtIhn/reMd+es5Zah3Tl/UHuSNFy0zF78agV3jp1Du6b1efmqARzcslG8QxIREZEYUY+giJRJ9q58Hv9kMU98uoR6dZL4zUlduSQByjosXLuN29+ew9dLN9G3fTr3nn4IfdqVXqaiNsvNL+CecfP4z5fLObZrCx69oD9N0lLiHZaIiIhUkoaGikjUuDsfzFvH3ePmsTozm5/2b8ttJ3enZePUeIe2m7vz9rdB4fqN23dy4WEduDksXC9725i1k2tf+Iavl27i6mMO5JZh3bXojoiISA2hoaEiEhXLN27nrrFz+XjhBrq2asgrVx2RkDXlzIwz+rflhB4tefiDRTz7RTBc9NZh3Tl7QDsNFw3NzdjCVc9N54esnfz1vH6c0b9tvEMSERGROFGPoIjsIyc3n3988j3/+PR7UpKMX/+kK5cd1YmUOA8DLat5GVu54+05TFu+mUM7pHPP6b3o1bZJvMOKq/GzMrjptZk0rV+XJy4ZoOGzIiIiNZCGhopIhX20YB13jZ3Hik07GNG3DX8Y3oNWCTQMtKwKCpw3Z6zm/nfms3nHLi45oiO/OalbrZsLV1DgPPTBQh77+HsGdGzKPy4+lJaNqt/rKSIiIqXT0FARKbeVm3Zwz/h5fDBvHQe1aMCLVx7OUQc3j3dYFZaUZJw9oB0/6dmKh95fyH++XM6E2Wu47eQenHlo21pRJ29rTi6/fvlbPlywnvMHtefu0w+hXp3keIclIiIiCUA9giK1XE5uPk99toS/f7yY5CTjhiFduGJwZ+rWqR7DQMtqzuot3P4iYS9uAAAgAElEQVT2HGasyGRQp6bcc3overRuHO+wqsySDVn84rlpLN+4gztP68nFR3SsFcmviIhIbaahoSJSJp8u2sCdb89h2cYdDO/dmj8M70Gb9LR4h1VlCgqc16evYtR7C9iSnctlR3bi1z/pQqPUmjVc9JOF67n+pRmkJCfx2IWHcuRBibfAj4iIiESfhoaKyH6tzszm3nHzeG/uWg5s3oDnrjiMY7q2iHdYVS4pyTh3UHtOOqQVoycu5N9fLGXcrAz+b3gPRvRtU+17zNydJz9bwp/fW0DXVo146tKBtG9WP95hiYiISAJSj6BILbIrr4B/TV7C3z5cjONcf0IXrvxx51o7b2zmykxuf3sOs1Zt4fDOzbj3jF50bdUo3mFVSE5uPr97YxZvf5vBKb1/xIPn9KV+XX3XJyIiUptoaGgCeGvGakZPXEhGZjZt0tO4eWi3hKvZlegxKr7KKR7fiL5tmDhvLUs2bGfoIa24/dSetGuq3qL8AueVqSt5YOICsnLyuOLoztwwpAuT5q2rNq9vq8b1qJOUxKrMbG46qSu/PP7gat+7KSIiIuWnRDDO3pqxmtvenE12bv7ubWkpydx/Zu+E+SCZ6DEqvsqJFB/AAQ1SePDcfhzfrWWcIktcm7bv4oH3FvDy1JU0Tk0mO7eA3Pw9fx+rw+v786M7c/upPeMUlYiIiMSbEsE4GzzqI1ZnZu+zvUlaHW4Y0jUOEe3r0Q8XsSU7b5/tiRKj4quckuJr0ySVL24bEoeIqo9vVmzmvCem7JUEFkr017dtehr/u/WEOEQkIiIiiUCJYJx1vnUCNeOsSk1jwNJRw+MdRsKrru9hvb4iIiK1W8KvGmpmw4BHgGTgX+4+qtj9xwB/BfoA57v767GPsuLapKdF7BFs3SSV9248Jg4R7WvYXz9jzZacfbYnSoyKr3JKiq8ml4aIpkR/D+v1FRERkcqKeSJoZsnAY8BPgFXAVDMb6+7ziuy2ArgcuCnW8UXDzUO7RZw/9rth3WmSlhj1yn43rHtCx6j4Kqek+G4e2i2OUVUfif4e1usrIiIilRWPHsHDgMXuvgTAzF4GTgd2J4Luviy8ryAO8VVa4WISibziYKLHqPgqJ9HjS3SJfv4SPT4RERFJfDGfI2hmZwPD3P3K8PYlwOHuPjLCvmOA8WUZGppIcwRFRERERERirTxzBJOqOpgIIhW3qlA2amZXmdk0M5u2YcOGSoYlIiIiIiJSO8QjEVwFtC9yux2QUZGG3P1Jdx/o7gNbtGgRleBERERERERqungkglOBLmbW2czqAucDY+MQh4iIiIiISK0U80TQ3fOAkcBEYD7wqrvPNbN7zGwEgJkNMrNVwDnAE2Y2N9ZxioiIiIiI1FRxqSPo7u8A7xTbdkeR36cSDBkVERERERGRKIvH0FARERERERGJo5iXj6gqZrYBWF7Kbk2ALWVssqz7lrZfc+CHMj5mdVaec1tdY4hW+5Vtp7zHx+O6h9px7eu6j11bVXndl3V/XfeBRLjuoWrjqK7XfXmP0XVfdrXhuo9m+9X9ui/Lfol63Xd097KtounuteYHeDLa+5a2HzAt3s870c5tdY0hWu1Xtp3yHh+P6z7cp8Zf+7ruY9dWVV73Zd1f1330r4lEjaO6XvflPUbXfXyuiUSOIxE+6yTCdV+W/WrCdV/bhoaOq4J9y9NmTZYI56GqY4hW+5Vtp7zH67qvOolwHqrLdV/Ztqryui/r/onweieCRDkPVRlHdb3uy3uMrvuyS5TzUF3+5lf3676icVQrNWZoaKIys2nuPjDecYjEmq59qY103UttpOteaqOacN3Xth7BeHgy3gGIxImufamNdN1LbaTrXmqjan/dq0dQRERERESkllGPoIiIiIiISC2jRFBERERERKSWUSIoIiIiIiJSyygRFBERERERqWWUCMaZmTUws+lmdmq8YxGJBTPrYWb/NLPXzezaeMcjEitmdoaZPWVmb5vZSfGORyQWzOxAM3vazF6PdywiVSn8TP9s+Hf+onjHUxZKBCvIzJ4xs/VmNqfY9mFmttDMFpvZrWVo6nfAq1UTpUh0ReO6d/f57n4NcC5QrevvSO0RpWv/LXf/BXA5cF4VhisSFVG67pe4+8+rNlKRqlHO98CZwOvh3/kRMQ+2ApQIVtwYYFjRDWaWDDwGnAz0BC4ws55m1tvMxhf7aWlmJwLzgHWxDl6kgsZQyes+PGYEMBn4MLbhi1TYGKJw7Yf+LzxOJNGNIXrXvUh1NIYyvgeAdsDKcLf8GMZYYXXiHUB15e6fmVmnYpsPAxa7+xIAM3sZON3d7wf2GfppZscDDQguomwze8fdC6o0cJFKiMZ1H7YzFhhrZhOAF6suYpHoiNLffANGAe+6+zdVG7FI5UXrb75IdVWe9wCwiiAZ/JZq0tmmRDC62rLnmwAILojDS9rZ3f8AYGaXAz8oCZRqqlzXvZkdRzB8oh7wTpVGJlK1ynXtA9cDJwJNzOxgd/9nVQYnUkXK+zf/AOCPQH8zuy1MGEWqs5LeA48Cfzez4cC4eARWXkoEo8sibPPSDnL3MdEPRSRmynXdu/snwCdVFYxIDJX32n+U4IOCSHVW3ut+I3BN1YUjEnMR3wPuvh34WayDqYxq0W1ZjawC2he53Q7IiFMsIrGi615qK137Uhvpupfarsa8B5QIRtdUoIuZdTazusD5wNg4xyRS1XTdS22la19qI133UtvVmPeAEsEKMrOXgClANzNbZWY/d/c8YCQwEZgPvOruc+MZp0g06bqX2krXvtRGuu6ltqvp7wFzL3UKm4iIiIiIiNQg6hEUERERERGpZZQIioiIiIiI1DJKBEVERERERGoZJYIiIiIiIiK1jBJBERERERGRWkaJoIiIiIiISC2jRFBERKLGzB42sxuL3J5oZv8qcvshM/tNKW18UYbHWWZmzSNsP87MjirhmBFmdmsp7bYxs9fD3/uZ2SnlPP5yM/t7+Ps1ZnZpac+ltOdQ0XaqQhjb+HjHISIilVcn3gGIiEiN8gVwDvBXM0sCmgONi9x/FHBjpAMLuXvERK6MjgOywjiKtzsWGFvKY2cAZ4c3+wEDgXfKenyxtv5Z1n2LOY4iz6ES7YiIiJRIPYIiIhJN/yNI9gAOAeYA28ysqZnVA3oAMwDM7GYzm2pms8zs7sIGzCwr/DfJzB43s7lmNt7M3jGzs4s81vVm9o2ZzTaz7mbWCbgG+LWZfWtmPy4aWLHeujFm9qiZfWFmSwrbNbNOZjbHzOoC9wDnhW2dV+z408zsKzObYWaTzKxV8RNhZneZ2U1hL+O3RX7yzaxjpDYiPYfCdsI2+5nZl+E5+6+ZNQ23f2Jmfzazr81sUfHnHu7T2sw+C9udU7iPmQ0Lz+NMM/sw3HZYeG5mhP92i9BeAzN7JnwNZ5jZ6SVeFSIiknCUCIqISNSEPWp5ZtaBICGcAnwFHEnQuzbL3XeZ2UlAF+Awgp63AWZ2TLHmzgQ6Ab2BK8M2ivrB3Q8F/gHc5O7LgH8CD7t7P3f/vJRwWwNHA6cCo4o9j13AHcArYVuvFDt2MnCEu/cHXgZuKelB3D0jbKMf8BTwhrsvj9RGGZ7Dc8Dv3L0PMBu4s8h9ddz9MIIe1zvZ14XAxDCOvsC3ZtYijOksd+9L0JsLsAA4JoztDuBPEdr7A/CRuw8CjgdGm1mDks6DiIgkFg0NFRGRaCvsFTwK+AvQNvx9C3uGbJ4U/swIbzckSAw/K9LO0cBr7l4ArDWzj4s9zpvhv9MJksbyeitse16kHr1StANeMbPWQF1gaWkHmNlggoS2sLeuXG2YWRMg3d0/DTc9C7xWZJei56NThCamAs+YWQrBc//WzI4DPnP3pQDuvinctwnwrJl1ARxIidDeScCIwt5KIBXoAMzf3/MQEZHEoB5BERGJti8IEr/eBENDvyTozTuKIEkEMOD+wp4ydz/Y3Z8u1o6V8jg7w3/zqdgXmzuL/F7aYxX3N+Dv7t4buJogCSpRmOw9DZzn7lkVaaMM9ns+3P0z4BhgNfCfcAEaI0j0irsX+NjdewGnlRCbEfQkFr6GHdxdSaCISDWhRFBERKLtfwTDLTe5e37Yy5ROkAxOCfeZCFxhZg0BzKytmbUs1s5k4KxwrmArgkVUSrMNaBSF51BaW00IEiqAy/bXSNgD9yrBkM5FZWgj4uO6+xZgc5H5f5cAnxbfbz9xdATWu/tTBEnpoQSvx7Fm1jncp1mE2C4vocmJBPM0LTy2f1ljERGR+FMiKCJSQWbWwcyyzCw5Cm2NMbP7ohFXCe2XOdYoPK/ZBKuFflls2xbgQTO7z93fB14EppjZbOB19k1+3gBWEfQqPkEw13BLKY89DvhppMViKuBjoGfhYjHF7rsLeM3MPgd+KKWdwmGyjxZZMKbNfto4lyBJjvQcLiOYizeLYG7lPREe70bggAjbjyOYFzgDOAt4BPgFMBd408xmAoVzIR8A7jezhQQJZyT3EgwZnWVmcwiGuT4P0X1vFBUunrMwmm1WlXABnyvjHYeISEnMPdKIEBERKWRmy4BWBEPuCnUNF0aJ1mOMAVa5+/9FuO9y4Ep3Pzpajxcv+3ueJezf0N2zzOwA4GtgsLuvrcoY483MPgGed/d/RbivE8FcwhR3z4vy41aqbTO7CzjY3S+OYkwOdHH3xdFqM1b29zqKiCQCLRYjIlI2p7n7pHgHURIzS3b3/NL3rHbGm1k6wWIq99b0JFBERCRWNDRURKSCLKg552ZWJ7z9iZnda2b/M7NtZva+mTUvsv9rZrbWzLaE9dwOKcNj9CAoJ3BkONQuM9w+xsz+YUFtve3A8WY2PKznttXMVoY9NOWOtQLP61IzW25mG83sdjNbZmYnlvEc/sLMFpvZJjMbGw6ZxAIPAz2BzkAeMC287xQzmxfGstr2rFpZtN16ZpZpZr2KbGthZtlm1tLMmltQmzAzfOzPzWyf/xPN7G4z+1v4e4qZbTezB8LbaWaWY3tq+R1hQc29TAtq8h1XpJ3dwwTNLNnMHjKzH8xsqZmNLHq+Qx1LON+Fq6pmhtdD8ZIahfULC4doFr6Wl5nZivAx/xBp30htW1A7cXKR/R8Jr62tZjbdShh+W/QaCtvJKvKTY0Eve2G9winhOVtjZn+3oIYjZlYYz8zwuPPM7DgzW1XkcXqE5zbTgnqTI4rcN8bMHjOzCeF5/MrMDioh3lQzez68hjMtqI3YKryvmZn928wyzGyzmb0Vbm8aXkMbwu3jzaxdpPbD/a8ws/nhvhMtmLMpIhI3SgRFRKLrQuBnQEuCXqyiScq7BCUSWgLfAC+U1li4CuM1wBR3b+ju6cUe648Ec+smA9uBSwkWZhkOXGtmZ1Qw1jLta2Y9gceBiwjq8jUhKBdRKjM7AbifYE5ca2A5QT09CEoTHAN0DZ/PecDG8L6ngavdvRHQC/ioeNvuvpOgnMIFRTafC3zq7uuB3xLMP2xBMOz390RePfNT9ixSMwhYCxwb3j4SWOjum82sLTABuA9oRnB+3rCgTl9xvwBOJpjjdygQ6TUq6bUprLWYHl4PUyIcG8nRQDdgCHCHBV8wFFeWtqeGcTcjmOP5mpntd7VTdy+8dhsCTQnmjr4U3p0P/JpgTumRYXzXhccVxtM3PH6vWo4WLMIzDnif4DxdD7xgZt2K7HYBcHf4uIsJ3i+RXEZw7bYnmF95DZAd3vcfoD5wSPg4D4fbk4B/Ax0JymZkA3+P1Hj4Pvw9QZmTFsDnRc6BiEhcKBEUESmbt8KegszCHoES/NvdF7l7NsFKkf0K73D3Z9x9W5ik3AX0taA2XEW97e7/c/cCd89x90/cfXZ4exbBB81j93N8ibGWY9+zgXHuPrlIEfayTj6/CHjG3b8Jz8ltBD2fnYBcggS3O8F89vnuviY8LpdgEZfG7r7Z3b8pof0X2TsRvDDcVthGa6Cju+e6++ceedL8FKCLBXMUjyFIQttasNrpsexZtfNi4B13fyc8/x8Q9GCeEqHNc4FH3H2Vu2+mWDH7UHlem7K4292z3X0mMJOgoHy5ufvz7r7R3fPc/SGgHkGCWVaPEnxh8Yewvenu/mXY3jKCRYH2d80WdQRB/clR7r7L3T8CxrP3a/6mu38dznl8gZLPYy5BAnhwuNLtdHffakHZj5OBa8JrLbewjmN4Ht5w9x3uvo0gySwp9qsJyqXMD2P5E9BPvYIiEk9KBEVEyuYMd08Pf/bXy1Z0DtsOgg+qhcMBR5nZ92a2FVgW7tOciltZ9IaZHW5mH4dD1bYQ9Grsr/2IsZZz3zZF43D3HezpuStNG4JewMJjs8Jj24Yf6v8OPAasM7MnzaxxuOtZBAnWcjP7NNLwyNBHQFp4XjoSJAH/De8bTdBD9L6ZLTGzWyM1ECZi0wg+4B9DkPh9AQxm70SwI3BOkS8LMgl64VqX8LyLvnYrI+xTntemLKLSnpn9NhzeuCV8jk0o4zVsZlcT9K5e6O4F4bau4ZDKteH74k9lbY/wPBa2FVrO3j3SZX3e/yEoh/FyOAT0gbDHsT1BGZTNEZ5PfTN7woJh0VsJhtamW+SVUjsCjxS5NjYR1GEsU++5iEhVUCIoIhIbFwKnAycSfHjuFG4vSyHzknrYim9/ERgLtHf3JgRzC8tbKL281gC750WZWRqRSxdEkkHwAbnw2AbhsasB3P1Rdx9AMCSvK3BzuH2qu59OMEzvLYIes32ECcKrBD1EFwLjw54bwp7Z37r7gQQF039jZkNKiPNT4ASgP8HQyE+BocBh7JlXtxL4T5EvC9LdvYG7R+rt2+ucESQbZVWVS33vt+1wPuDvCHo0m4bDlLdQhmssPPZe4PSwHmKhfwALCFYGbUwwfLKs12wG0N72ntvZgT31D8ss7Om72917EpT6OJVgmPVKoJkFCxYV91uC3tDDw9gLh7JGin8lwXDmotdHmrt/Ud5YRUSiRYmgiEhsNAJ2EvR41Sfo+SirdUC7wkU0SnmMTe6eY2aHESQ/Ve114DQzOyqM727K/kH+ReBnZtbPzOoRnJOv3H2ZmQ0Ke/JSCIYS5gD5ZlbXzC4ysybungtsZe+yHpEe4zyCYaiFw0Ixs1PN7GAzsyJtlNTOpwRJwbxw+OsnwJXAUnffEO7zfHgehoa9v6nhwiaRFg95FfiVmbUNE4zflXqm9tgAFAAHluOYaLXdiGDRng1AHTO7A2hcwr67mVl7gvqEl7r7oghtbgWyzKw7cG2x+9ftJ56vCK6NWyxYyOc4gqT+5RL231+Mx5tZ77A3byvBUNH8cDjyu8Dj4eIwKWZWmPA1IpgXmGlmzYA79/MQ/wRus3CBKDNrYmbnlDdOEZFoUiIoIhIbzxEMW1sNzGPvYuul+Yig6PdaM9tf8fLrgHvMbBvBXL2IPWXR5O5zCRbpeJmgp2sbsJ4g6S3t2A+B2wkKx68BDgLOD+9uDDwFbCY4bxuBB8P7LgGWhcPxriGYn1fSYxQmC20IPtAX6gJMArII5gE+7u6flNDMF0Aae3r/5hEkpoW3cfeVBD2+vydIlFYS9GBG+n/2KYIFTmYBM4B3CBKsUst/hENv/wj8LxxmeERpx5RVGdqeSHAOFxG8JjlEHtZa3BDgR8Drtmfl0LnhfTcRfGGxjeC8vFLs2LuAZ8N4zi0W7y5gBMEcvh8IFi261N0XlOX5FvMjgi81tgLzCZL/wtVULyFIDBcQXNs3htv/SnBd/EDwfn6vpMbd/b/AnwmGnm4F5oRxi4jEjQrKi4hI1ISLqGQSDPVbGu94qgMzOxn4p7tr4RAREYkZ9QiKiEilmNlp4cIZDQh67WazZzEcKcaC+oOnWFBjry3BkML/lnaciIhINMU8EQznTXxtQbHduWZ2d4R9Lg9Xvfs2/Lky1nGKiEiZnU6wcEcGwZDL80soxSABI5hLuZlgaOh8gqG8IiIiMRPzoaHhxPwG7p4VLgIwGfiVu39ZZJ/LgYHuPjKmwYmIiIiIiNQCdWL9gOG3xFnhzZTwR98ci4iIiIiIxEjME0EICisD04GDgcfCVd2KOytconkR8OtwRbbi7VwFXAXQoEGDAd27d6/CqEVEREQkkSzfuINdeQV0adUw3qGIJITp06f/4O4tyrJvXFcNDesn/Re43t3nFNl+AJDl7jvN7BrgXHc/YX9tDRw40KdNm1a1AYuIiIhIwrjy2ams2ZLDhBt+HO9QRBKCmU1394Fl2Teuq4a6eyZBYd5hxbZvdPfCGlRPAQNiHJqIiIiIJLj8AifJLN5hiFRL8Vg1tEXYE4iZpQEnEhRpLbpP6yI3RxCsqCYiIiIisluBQ1KSEkGRiojHHMHWwLPhPMEk4FV3H29m9wDT3H0scIOZjQDygE3A5XGIU0REREQSWIE7ygNFKiYeq4bOAvpH2H5Hkd9vA26r7GPl5uayatUqcnJyKtuU7Edqairt2rUjJSUl3qGIiIhILRIkgsoERSoiLquGxsqqVato1KgRnTp1wvRHokq4Oxs3bmTVqlV07tw53uGIiIhILVJQAMn6jCdSIXFdLKaq5eTkcMABBygJrEJmxgEHHKBeVxEREYm5fHf0MU+kYmp0IggoCYwBnWMRERGJB9fQUJEKq/GJoIiIiIjUTAUOyVotRqRClAhWsWXLltGrV68qafuTTz7h1FNPBWDs2LGMGjWqSh5HREREJBHlF2hoqEhF1ejFYsrrrRmrGT1xIRmZ2bRJT+Pmod04o3/beIdVJiNGjGDEiBHxDkNEREQkZtxdPYIiFaQewdBbM1Zz25uzWZ2ZjQOrM7O57c3ZvDVjdaXbzsvL47LLLqNPnz6cffbZ7Nixg3vuuYdBgwbRq1cvrrrqKtwdgEcffZSePXvSp08fzj//fAC2b9/OFVdcwaBBg+jfvz9vv/32Po8xZswYRo4cCcDll1/ODTfcwFFHHcWBBx7I66+/vnu/0aNHM2jQIPr06cOdd95Z6ecmIiIiEi8FjuYIilRQrekRvHvcXOZlbC3x/hkrMtmVX7DXtuzcfG55fRYvfb0i4jE92zTmztMOKfWxFy5cyNNPP83gwYO54oorePzxxxk5ciR33BGUTrzkkksYP348p512GqNGjWLp0qXUq1ePzMxMAP74xz9ywgkn8Mwzz5CZmclhhx3GiSeeuN/HXLNmDZMnT2bBggWMGDGCs88+m/fff5/vvvuOr7/+GndnxIgRfPbZZxxzzDGlPgcRERGRRJNfoILyIhWlHsFQ8SSwtO3l0b59ewYPHgzAxRdfzOTJk/n44485/PDD6d27Nx999BFz584FoE+fPlx00UU8//zz1KkT5Onvv/8+o0aNol+/fhx33HHk5OSwYkXk5LTQGWecQVJSEj179mTdunW723n//ffp378/hx56KAsWLOC7776r9PMTERERiQcVlBepuFrTI1haz93gUR+xOjN7n+1t09N45eojK/XYxcsrmBnXXXcd06ZNo3379tx111276/BNmDCBzz77jLFjx3Lvvfcyd+5c3J033niDbt267dVOYYIXSb169Xb/Xjjs1N257bbbuPrqqyv1fEREREQSgRJBkYpTj2Do5qHdSEtJ3mtbWkoyNw/tVsIRZbdixQqmTJkCwEsvvcTRRx8NQPPmzcnKyto9h6+goICVK1dy/PHH88ADD5CZmUlWVhZDhw7lb3/72+6EbsaMGRWKY+jQoTzzzDNkZWUBsHr1atavX1/ZpyciIiISFwUOSfo0K1IhtaZHsDSFq4NWxaqhPXr04Nlnn+Xqq6+mS5cuXHvttWzevJnevXvTqVMnBg0aBEB+fj4XX3wxW7Zswd359a9/TXp6Orfffjs33ngjffr0wd3p1KkT48ePL3ccJ510EvPnz+fII4MezoYNG/L888/TsmXLSj9HERERkVhTj6BIxVlhL1N1N3DgQJ82bdpe2+bPn0+PHj3iFFHtonMtIiIisXbc6I/p0y6dRy/oH+9QRBKCmU1394Fl2Ved6SIiIiJSLRU4qiMoUkFKBEVERESkWipwRyNDRSpGiaCIiIiIVEsFBZojKFJRSgRFREREpFoqcEhWIihSIUoERURERKRayndX+QiRCtJbR0RERESqJXfH1CMoUiFKBKvYsmXL6NWrV5n3HzNmDBkZGaXuM3LkyMqGJiIiIlKtaWioSMUpESxq1qvwcC+4Kz34d9arMQ+hLIlgVcnLy4vL44qIiIhURH6Bo+oRElMJkC9ES514B5AwZr0K426A3Ozg9paVwW2APudWqum8vDwuu+wyZsyYQdeuXXnuued48MEHGTduHNnZ2Rx11FE88cQTvPHGG0ybNo2LLrqItLQ0pkyZwpw5c/jVr37F9u3bqVevHh9++CEAGRkZDBs2jO+//56f/vSnPPDAAwA0bNiQX/3qV4wfP560tDTefvttWrVqxfLly7niiivYsGEDLVq04N///jcdOnTg8ssvp1mzZsyYMYNDDz2URo0asXTpUtasWcOiRYv4y1/+wpdffsm7775L27ZtGTduHCkpKZU6HyIiIiLRUOBOkjJBiZUqzBfiofb0CL57K/x7eMk/b4/c86IWys0Otpd0zLu3lumhFy5cyFVXXcWsWbNo3Lgxjz/+OCNHjmTq1KnMmTOH7Oxsxo8fz9lnn83AgQN54YUX+Pbbb0lOTua8887jkUceYebMmUyaNIm0tDQAvv32W1555RVmz57NK6+8wsqVKwHYvn07RxxxBDNnzuSYY47hqaeeAmDkyJFceumlzJo1i4suuogbbrhhd3yLFi1i0qRJPPTQQwB8//33TJgwgbfffpuLL76Y448/ntmzZ5OWlsaECRMq+0qIiIiIRIU7Kh8hsfPhPZHzhQ/viU88lVR7EsHS5O8s3/ZyaN++PYMHDwbg4osvZvLkyXz88cccfvjh9O7dm48++oi5c+fuc9zChQtp3bo1gwYNAqBx48bUqRN04g4ZMoQmTZqQmqbf6FIAACAASURBVJpKz549Wb58OQB169bl1FNPBWDAgAEsW7YMgClTpnDhhRcCcMkllzB58uTdj3POOeeQnJy8+/bJJ59MSkoKvXv3Jj8/n2HDhgHQu3fv3e2JiIiIxJuGhkpMbVlVvu0JrvYMDT151P7vf7hX0L1bXJP28LPK9YIVX83KzLjuuuuYNm0a7du356677iInJ2ef4/a3Ela9evV2/56cnLx7fl9KSsruY4pu319MDRo0iNh2UlLSXu0lJSVpHqGIiIgkDA0NlZhq0q6EfKFd7GOJAvUIFhpyB6Sk7b0tJS3YXkkrVqxgypQpALz00kscffTRADRv3pysrCxef/313fs2atSIbdu2AdC9e3cyMjKYOnUqANu2batwInbUUUfx8ssvA/DCCy/sjkFERESkuipw19BQiZ0hd0BysbUyopQvxEPt6REsTeEEzw/vCbp3m7QLXtQoTPzs0aMHzz77LFdffTVdunTh2muvZfPmzfTu3ZtOnTrtHvoJcPnll3PNNdfsXizmlVde4frrryc7O5u0tDQmTZpUoRgeffRRrrjiCkaPHr17sRgRERGR6qzA0dBQiZ0+5wa5wra1UJAX1XwhHszd4x1DVAwcONCnTZu217b58+fTo0ePOEVUu+hci4iISKx1vm0C1x9/ML85qVu8Q5HaIGMGPHkcnPIgHPaLeEcTkZlNd/eBZdlXQ0NFREREpNpxd9z3XYtBpMpMHwN10qD3OfGOJCqUCIqIiIhItVMQDmrTHEGJiZ3bYPbr0OtMSEuPdzRRUeMTwZoy9DWR6RyLiIhIrBWEnz+Sa/ynWUkIc96AXVkw4PJ4RxI1Nfqtk5qaysaNG5WoVCF3Z+PGjaSmpsY7FBEREalF8sMuQQ0NlZiYPgZa9oR2g0rdtbqo0auGtmvXjlWrVrFhw4Z4h1Kjpaam0q5d9ayfIiIiItVT4ff8yVo2VKpaxrfBQjEnPwA16IuHGp0IpqSk0Llz53iHISIiIiJRlh9mgsoDpcp98yzUSa22ZSJKUqOHhoqIiIhIzVSwOxFUJihVaGcWzHoNDvkppDWNdzRRpURQRERERKodLwj+VSIoVWrum7BrW41aJKaQEkERERERqXY0NFRiYvoYaNED2h8e70iiTomgiIiIiFQ7u4eGKhOUqrJmFqyeHvQG1sCeZyWCIiIiIlLtaI6gVLkaukhMoZgngmaWamZfm9lMM5trZndH2Keemb1iZovN7Csz6xTrOEVEREQkcRVojqBUpV3bYdar0PMMqN8s3tFUiXj0CO4ETnD3vkA/YJiZHVFsn58Dm939YOBh4M8xjlFEREREElhhj2CyxrdJVZj7X9i5tUYuElMo5m8dD2SFN1PCHy+22+nAs+HvrwNDzPR1j4iIiIgE8v+fvfuOr6q+/zj++maHTFaAhD1kyAYVBa0bB4haq7bu2rqtXdafrVVrh7Z2qnW1KlVxiwwXKk5QUMKeCsgMGxICZN/v749zb3KT3ECSe5Nzx/v5eJzHvffcc24+uTmB+853eZyPj/qIKC0ifzJ06A/d67ZXRQ9X/oZijIk3xiwGdgLvW2vn1zkkD9gMYK2tBIqA9gFe5zpjzAJjzIJdu3a1dNkiIiIiEia8DYLqGiqht305bPkqaieJ8XElCFprq6y1w4GuwLHGmMF1Dgn0jtdtNcRa+6S1drS1dnTHjh1bolQRERERCUPqGiotJn8yxCfDsEvdrqRFufqrY60tBD4Gzqrz1BagG4AxJgHIAva2anEiIiIiEraqNGuotITyQ7D0ZRg0KWonifFxY9bQjsaYbO/9VOB0YHWdw2YAV3nvXwR8aK2t1yIoIiIiIrHJ99FQYwQlpGJgkhifBBe+Zhfgf8aYeJwg+oq19k1jzH3AAmvtDOAp4DljzFqclsDobpcVERERkSbxzhVDvIKghFL+ZGjfD3qc4HYlLa7Vg6C1dikwIsD+u/3ulwLfa826RERERCRy+GYNjVMOlFDZsQK2fAln/jGqJ4nx0fBaEREREYk4vsli4pQEJVTy/wfxSTDs+25X0ioUBEVEREQk4ng8zq0mi5GQKD8ES1+CgedBWr1V66KSgqCIiIiIRJzqFkHlQAmFldOhtCgmJonxURAUERERkYijrqESUvmToV0f6DnO7UpajYKgiIiIiEQcj9YRlFDZuQo2z3NaA2PoelIQFBEREZGI41s+Qg2CEjTfJDHDf+B2Ja1KQVBEREREIo7HmwS1jqAEpaIElrwIAydCWge3q2lVCoIiIiIiEnGqvF1DjYKgBGPlDCgtjKlJYnwUBEVEREQk4lh1DZVQyJ8M7XpDzxPdrqTVKQiKiIiISMSp8nUNVRKU5tq1BjZ9HnOTxPgoCIqIiIhIxPGoa6gEK/9/EJcIw2JrkhgfBUERERERiTi+rqFqEZRmqSiFJS/AwAmQ3tHtalyhICgiIiIiEcfXNVQ5UJpl1Qwo2ReTk8T4KAiKiIiISMTRgvISlPzJ0LYX9DzJ7UpcoyAoIiIiIhGnZkF5BUFpol1fw8a5MOoqiIvdOBS737mIiIiIRKzqFkF9mpWmWvg/iEuA4Ze5XYmr9KsjIiIiIhFHXUOlWSpKYfELMOBcSM9xuxpXKQiKiIiISMSpmSxGQVCaYPWbULI3pieJ8VEQFBEREZGIY6vHCLpbh0SY/MmQ3QN6nex2Ja5TEBQRERGRiOPrGqp1BKXRdq+FDZ/F/CQxPnoHRERERCTiqGuoNNnCyd5JYi53u5KwoCAoIiIiIhHH1zVUOVAapbLMmSSm/9mQ0cntasKCgqCIiIiIRBx1DZUmWf0mHNqjSWL8hCQIGmPijDGZoXgtEREREZEjqdLyEdIU+ZMhuzv0PtXtSsJGs4OgMeYFY0ymMSYNWAmsMcbcHrrSREREREQC86hrqDTWnnXw7acwUpPE+AvmnRhkrd0PnA+8DXQHrghJVSIiIiIih+HxJsF4JUE5koX/AxMPIzRJjL9ggmCiMSYRJwhOt9ZWADY0ZYmIiIiINMyjrqHSGJXlsGiKd5KYzm5XE1aCCYJPABuANOBTY0wPYH8oihIRERERORxf19A4TRYjh7PmLTi0G0Zd43YlYSehuSdaax8CHvLbtdEYc0rwJYmIiIiIHJ6neh1BlwuR8JY/GbK6Qx/FlLqCmSzmNu9kMcYY85QxZiGgaXhEREREpMWpa6gc0Z51sP5jGHklxMW7XU3YCaZr6A+9k8WcCXQErgEeCElVIiIiIiKH4esaqnUEpUELn/VOEnOZ25WEpWCCoO+37hzgGWvtEr99IiIiIiItxtciqAZBCaiyHBZPgaPOgsxct6sJS8EEwXxjzHs4QXCWMSYD8ISmLBERERGRhtWMEVQSlADWvA0Hd8Goq92uJGw1e7IY4FpgOLDeWnvIGNMep3uoiIiIiEiLqrJaR1AOI38yZHaFvqe5XUnYCmbWUI8xpivwA+P8An5irZ0ZsspERERERBrgGyOoHCj17P0W1n8EJ/9ak8QcRjCzhj4A3Aas9G4/McbcH6rCREREREQaYq0lzoBREpS6Fj4LJg5GXO52JWEtmK6h5wDDrbUeAGPM/4BFwJ2hKExEREREpCFVHqvxgVJfVQUseh76jYesPLerCWvBTBYDkO13PyvI1xIRERERaRSP1UQxEsCad+DgTk0S0wjBtAjeDywyxnyEs2zESag1UERERERagbWWuGCbNCT65E+GzDzoe7rblYS9Zv/6WGtfBMYAU73b8dbal450njGmmzHmI2PMKmPMCmPMbQGOOdkYU2SMWezd7m5unSIiIiISfdQ1VOrZtwHWfQgjroD4YNq7YkOT3yFjzMg6u7Z4b3ONMbnW2oVHeIlK4BfW2oXetQfzjTHvW2tX1jnuM2vthKbWJyIiIiLRT11DpZ6FzznTyI68wu1KIkJzovLfDvOcBU493MnW2m3ANu/9YmPMKiAPZ+ZREREREZEj8nhnDRUB/CaJOROyurpdTURochC01p4Sqi9ujOkJjADmB3j6eGPMEqAA+KW1dkWA868DrgPo3r17qMoSERERkTDnsZY4JUHx+XoWHNiuSWKawLUhtsaYdOB14KfW2v11nl4I9LDWDgMeBqYFeg1r7ZPW2tHW2tEdO3Zs2YJFREREJGw4LYIKguKVPxkycqHvGW5XEjFcCYLGmEScEDjFWju17vPW2v3W2gPe+28DicaYDq1cpoiIiIiEqSqPxgiKV+EmWPuBMzZQk8Q0WqsHQWOMAZ4CVllr/97AMZ29x2GMORanzj2tV6WIiIiIhDOrMYLis/A553aEJolpimZH5gCzhwIUARuttZWHOXUscAWwzBiz2Lvv10B3AGvt48BFwI3GmEqgBLjUWmubW6uIiIiIRBePtcQrCUpVJSx6DvqdAdnd3K4mogTTdvooMBJYirOg/GDv/fbGmBuste8FOslaO8d7fIOstY8AjwRRm4iIiIhEMXUNFQC+mQXF2+Dcwy1sIIEE0zV0AzDCO1nLKJzZP5cDpwN/CUFtIiIiIiIBWWtRDhTyJ0N6Z+g33u1KIk4wQXCA/5IO3gXhR1hr1wdfloiIiIhIw6rUNVQKN8M372uSmGYK5h1bY4x5DHjJ+/gS4GtjTDJQEXRlIiIiIiIN8Fh1DY15izRJTDCCaRG8GlgL/BT4GbDeu68CCNmi8yIiIiIidXnUNTS2VVU6s4X2PQ3a9nC7mojU7BZBa20J8DfvVteBZlckIiIiInIEHo8lXkkwdq19H4oL4BxNTdJcwSwfMRa4F+jh/zrW2t7BlyUiIiIi0jCPteoaGsvyJ0N6JzjqLLcriVjBjBF8CqdLaD5QFZpyRERERESOzGMhTpPFxKaiLfDNezDuZxCf6HY1ESuYIFhkrX0nZJWIiIiIiDSSx2NRDoxRi54H64GRV7pdSUQLJgh+ZIx5EJgKlPl2WmsXBl2ViIiIiMhhqGtojPJUwcJnoc+p0Lan29VEtGCC4HHe29F++yxwahCvKSIiIiJyRFXqGhqb1n4A+7fCWQ+4XUnEC2bWUC0RcRjTFm3lwVlrKCgsITc7ldvH9+f8EXlul1VLuNeo+oIT7vWFu3B//8K9PhGRlmatuobGpPzJkJYD/c92u5KI1+QgaIy53Fr7vDHm54Get9b+PfiyItu0RVu5c+oySiqcOXS2FpZw59RlAGHzQS3ca1R9wQn3+sJduL9/4V6fiEhrUNfQGLS/AL5+F8b+VJPEhEBzWgTTvLcZoSwkmjw4a031BzSfkooqfvPGMhZt2odpxD9adQ8x1D+n/jFHfh1nn+GF+ZsC1njXtOWs2r7/sF/7SF/3SLUfsW5jeGbutwHr++205azdeQCLBcA6N9ha92t2eu9hra11rO/cuq/jOzbQa/qfP23R1oD1/fqNZXy5YW/19+T7Xn3vQc1j/2838Pvj/77VPa/6sd+5xu/OlHmBf74PzlqjoNAIf5m1OuD7d+fUZXy4emet/Zb6rLVHPKbuTltnhw1wkm/fx1/vpLTCU68+/XxFJJZUaR3B2KNJYkKqyUHQWvuE9/Z3oS8nOhQUlgTcf7C8immLC2rtq/uBEQJ8aAz0gbDu48a8DjUfJOt+yPU5UFbJ5LkbGl3LkT68HqnO+s8HLKtacVklj368FmNM4LAVIDD5h7Ca/abmOL9zqvcH2ue3/1B54PfvUHkV763YXut78Q+jdb/n6mPqPucfTBs4v17w9dtXVlk7JPgUFJY4/3GqL0091lpWFOxn+uKtFBSWBjympKKKZVuLAv7RJdDOwH+cCfBHnYDHBXo9Z2fdEOjT0L89IiLRyGMD/1spUco3SUzvU6BdL7eriQrBLCjfEfgx0JPaC8r/MPiyIltudipbA3wgy8tOZe7/hcdcOmMf+DCsa1R9wWmoPgscf/9szh3ahfOG5TK8W3ajWqij2bpdB5ixuICZSwpYv/sgifGGlIQ4SgOE6bzsVD765cmtX2QdDf18u2SluFCNiIg7rLUkxMe5XYa0lnUfQtFmOPMPblcSNYL57ZkOZAEfAG/5bTHv9vH9SU2Mr7UvNTGe28f3d6mi+sK9RtUXnED1pSTGcdXxPRjeLZsp8zZxwaOfc9KDH/GXd1ezuk534Gi3raiEJz9dx4SHP+O0v33CQx9+Q6fMFO6/cAhf/eZ0Hvju0Ij7+QLEGcP2osCtmSIi0abKozGCMSV/MqR1hP7nuF1J1Ahm+Yg21to7QlZJFPGN0QnnGf3CvUbVF5wj1be/tIJZy7czY0kBT3y6nkc/XsdRndKZODSXicNy6dkh7XAvH5H2Hizn7WXbmLGkgK827MVaGNY1i7vOHcjEYbl0yqxpTYvEn+/ZQzrz4vxNTPr3HP575TEM6ZrlcpUiIi1LXUNjyP5tsOYdOOFWSEhyu5qoYQKNLWvUicb8AfjcWvt2aEtqntGjR9sFCxa4XYZIxNl9oIx3lm1j5pJtfLlhLwBDu2Zx3rBczh3ahS5ZqS5X2HwHyip5f+V2Ziwu4LNvdlPpsfTNSee8YbmcF4WBd/X2/Vw7eQF7Dpbx94uHc86QLm6XJCLSYs57ZA7t0pKYfM2xbpciLe2TB+GjP8CtC6F9H7erCWvGmHxr7egjHxlcECzGmUG0DKjAme/AWmszm/WCQVIQFAleQWEJby11Ws2WbS3CGDimZzvOG5bL2YM70z492e0Sj6issoqP1+xixpICZq/aQWmFh7zsVCYM68KkYXkM7JIR1eMidx8o47pnF7BwUyG/OOMobjm1b1R/vyISuyY8/Bk5GSk8ffUxbpciLclTBf8a7kwQc9UMt6sJe00JgsEsKK/lI0SiTG52Kj8+qTc/Pqk33+4+yMwlBcxYUsBd05Zzz4wVjOvbgYnDchl/dCcyUsJn/Z4qj+WLdXuYsWQr7yzfTnFpJe3TkvjeqG5MGp7LyO5tiYuRmVI7pCfzwo/HcOfUZfzt/a9Zu+sAf/7uUFICjCkUEYlkHg9aUD4WrPsIijbBGVqwINSas6D8AGvtamPMyEDPW2sXBl+WiLitV4c0fnJaP249tS+rtxczY4kzs+YvX13Cr9+I49T+OUwclstpA3NcCRnWWhZtLmTG4gLeWraNXcVlpCcncObRnZg0PI+xfdrH7GxyKYnx/P3iYfTNSefBWWvYuOcQT145ipwMzSoqItFDC8rHiPxnoE0HGDDB7UqiTnNaBH8OXAf8LcBzFnB/7nwRCRljDAO7ZDKwSya/Gt+/Vvh6d8V20pLiOWNQJ84bnsu4vh1JSmjZ8PX1jmKmL97KzCXb2LT3EEkJTiidNDyXUwa4E0rDkTGGm0/pS5+Oafzs5SWc/8hc/nvVMQzKdaX3vohIyCkIxoDi7c4kMcffrEliWkBzFpS/znt7SujLEZFwZoxhZPe2jOzelt9OGMT89XuYubSAt5dtZ9riArLbJHL24M5MHJbLcb3ah2zh+s17D1W3SK7eXkx8nOGEPu35yWn9OPPoTmSGUTfVcHPW4C50bduGHz+7gIse/5x/XjKcM4/u7HZZIiJB81iIi82OH7Fj0fNgq2DkVW5XEpWaPVkMgDFmMDAIqO5vZK19NgR1NZkmixFxT3mlh8++2cXMJQW8t3IHh8qryMlIDmrh+l3FZby11BmjuHBTIQCjerRl0vBczhnShQ4RMHFNONm5v5QfP5fP0i2F/Gr8AG74Tm9NIiMiEe3Uv37MoNxMHvlBwNFKEuk8HnhoGGT3gKvfdLuaiNEqk8UYY+4BTsYJgm8DZwNzAFeCoIi4JykhjtMGduK0gZ0oKa/iw9U7mbFkK1Pmb+KZuRvo1i61eo3CAZ2dWTunLdpab52+Uwfm8O7y7cxcUsDctbvxWBjQOYM7zhrAxGFOy5Y0T05mCi9fN4ZfvrqEP7+7mrU7D/CnCweTnKCutCISmdQ1NEotfQVm3wdFWwAL/c50u6KoFczyEcuAYcAia+0wY0wn4L/W2omhLLCx1CIoEn72l1bw3oodzFxSwJy1u6nyWPrlpNM3J50PV++krNJTfWyccdagqbLQvV0bJg131vrr10kTFIeStZZ/zf6Gf37wDcf0bMvjl4+KiGVBRETqOvEvHzK6Rzv+cclwt0uRUFn6Csz8CVSU1OxLTIWJD8HQi92rK4K0SosgUGKt9RhjKo0xmcBOoHcQryciUSYzJZGLRnXlolFd2XOgjLe9rX3vLN9e71iPhbTkeKb8aAzDumap22ILMcbw09OPok/HdH756hIm/XsuT111DP07K3CLSGTxeED/VUSZ2ffVDoHgPJ59n4JgCwhmiO0CY0w28B8gH1gIfBmSqkQk6rRPT+aKMT145frjaej/7UNlVc0aTyhNN3FYLq9cfzzllR6++9jnfLR6p9sliYg0iVXX0OhTtKVp+yUozQqCxvmUdr+1ttBa+zhwBnCVtfaakFYnIlEpNzu1SfulZQzrls30W8bSo30brv3fV/z3s/UEM4GYiEhrqrKWeAXB6FFZDskN9E7J6tq6tcSIZgVB63xSmOb3eIO1dmnIqhKRqHb7+P6k1lnvLzUxntvH93epotjVJSuVV284njMHdeYPb63i128so9xv7KaISLjS8hFRZMcK+O+pULYfTJ1JzBJT4bS73akrygXz6zPPGHNMyCoRkZhx/og87r9wCHnZqRggLzuV+y8cwvkj8twuLSa1SUrg0ctGcsspfXnxy81c+fR89h0sd7ssEZHDstZqKEGkq6qEz/4OT3zHWTz+0hfggschqxtgnFtNFNNigpks5hTgemPMRuAgzoR/1lo7NCSViUhUO39EnoJfGImLM/xyfH/65KRxx2vLuODRufz3qmPom5PudmkiIgFVedQ1NKLtXgvTboAtX8GgSXDuPyCtvfOcgl+rCCYInh2yKkREJCxcMKIr3dulcf1zC7jg0bk8etlITuzX0e2yRETq8Vhn6SGJMB4PfPkkfHAvJCTDd5+Cwd/VFLAuCKZr6B+stRv9N+APoSpMRETcMapHW6bdPJa87FSufuYrnv1ig9sliYjU4/FY4pQEI8u+jfDsefDuHdDrRLhpHgy5SCHQJcEEwaP9Hxhj4oFRwZUjIiLhoGvbNrx24wmc0r8jd09fwW+nLaeySpPIiEj48Gj5iMhhLSx8Fh4bCwWL4LyH4QevQGYXtyuLaU0OgsaYO40xxcBQY8x+71aMs6D89JBXKCIirkhPTuCJK0Zz/Um9eW7eRq5+5iuKSircLktEBFDX0Iixfxu8cDHMuBVyh8ONn8PIK9UKGAaaHASttfdbazOAB621md4tw1rb3lp7ZwvUKCIiLomPM9x5zkD+ctFQ5n+7hwsencuG3QfdLktEhCqrrqFhzVpY9ho8Oga+/QzO+jNcOQPa9nC7MvFqdtdQhT4Rkdhx8ehuTPnRGPYdLGfSv+fy+brdbpckIjHOqmto+Dq4B169Gl6/Fjr0gxvmwJgbtPBjmNFPQ0REGuXYXu2YfvM4cjKSufKpL3nxy01ulyQiMUxdQ8PU6rfh0eNg9Vtw2j1wzbvQoa/bVUkArR4EjTHdjDEfGWNWGWNWGGNuC3CMMcY8ZIxZa4xZaowZ2dp1iohIfd3bt+H1m05gbN8O3Dl1GffNXEmVx7pdlojEIK0jGGZKCuGNG+Gl70N6Z7juYzjx5xAfzGp10pKa/JMxxrQ73PPW2r1HeIlK4BfW2oXGmAwg3xjzvrV2pd8xZwP9vNtxwGPeWxERcVlmSiJPXTWaP729mqfnfsv63Qd4+PsjyEhJdLs0EYkR1jp/gDIKguFh3Ucw/WYo3g4n3Q4n/QoSktyuSo6gORE9H7BAoN88C/Q+3MnW2m3ANu/9YmPMKiAP8A+Ck4BnrfNbPs8Yk22M6eI9V0REXJYQH8fdEwfRJyeNe6av4MJHP+fpq4+hW7s2bpcmIjHA1xMhXn1D3VV2AD64B776L3Q4Cq59H7pqNblI0eQgaK3tFaovbozpCYwA5td5Kg/Y7Pd4i3dfrSBojLkOuA6ge/fuoSpLREQa6bLjetCrfRo3TlnIpH/P5YkrRnFMz8N2HAkL0xZt5cFZaygoLCE3O5Xbx/fn/BF5bpclIo3k65GuHOiijV/AtBth3wY4/hY49S5ITHW7KmmCoMYIGmPaGmOONcac5NuacG468DrwU2vt/rpPBzil3iAUa+2T1trR1trRHTt2bFrxIiISEif07cC0m8eSnZrID/4zj1cXbD7ySS6atmgrd05dxtbCEiywtbCEO6cuY9qirW6XJiKN5FHXUPdUlMJ7d8EzZ4P1wNVvwvg/KgRGoGaP3jTG/Ai4DegKLAbGAF8Apzbi3EScEDjFWjs1wCFbgG5+j7sCBc2tVUREWlavDmm8cdNYbnohn9tfW8o7y7exensx2wpLQ97i5vFYyio9lFRUOVt5FaV+90sqnMel1Y891ftKyqt4feEWSiqqar1mSUUVv3ljGSsKikhJjK/eUhPjSUmM8976tjhSk+JJSYivvk1JiiMpPi5kH0rVYilyeL4gqK6hraxgEbxxA+xaDaOugTN/D8kZblclzRTMND63AccA86y1pxhjBgC/O9JJxvlf8ilglbX27w0cNgO4xRjzEs4kMUUaHygiEt6y2iQy+Zpjufrp+Xy4elf1/q2FJfzqtaUsLyhieLfsOsGtdkjzhbt6j2s952lWfamJTnA7VF4V8PmD5VVMmb+JkooqbDMmQjWG6sCYmhhPsl+A9AXKQAEzufpxPKlJcSzdXMiU+Zspr/JUv393Tl0GoDAo4qWuoa2sqgI+/St8+iCkd4LLX4e+p7tdlQQpmCBYaq0tNcZgjEm21q42xvRvxHljgSuAZcaYxd59vwa6A1hrHwfeBs4B1gKHgGuCqFNERFpJYnwcG/aU1NtfXuXhv599G/CcpPi46la2mkDk3M9KTSTFe98X5HxBKtXXMuf3XN3zffuTE2pa68Y+8CFbC+vXmJedytz/OxVrLeVVHkrLPZRW1m5lLKmocU9fuQAAIABJREFUosy/hdEbTP1bIJ1znHNLvY8PlVey96DH75ymhdqSiioenLVaQVDEy9ciqAXlW8GOlfDG9bB9KQy9FM5+AFLbul2VhEAwQXCLMSYbmAa8b4zZRyO6b1pr5xB4DKD/MRa4OYjaRETEJQUBQhY4//DP+tlJtcJaSkIcCfGtu6Tt7eP7c+fUZbW6h6YmxnP7eOdvmcYYkhPiSU6IJ4uWXRLDWqebq38wPPWvH9cfFA9sLSzlf59v4IKReWRqqQ6JcR6PgmCL81TB5w/DR3+E5Ey45HkYONHtqiSEmh0ErbUXeO/ea4z5CMgC3g1JVSIiErFys1MDtrjlZqdyVCf3x5L4WtXCYQyeMaa6u2i2d19D719ivOGeGSv487urmTQ8j8vHdOfo3KzWLVgkTKhraAvbs86ZEXTzfCf8TfgnpHVwuyoJsWBaBDHGxAOdAF9/n87ApmCLEhGRyHWkFrdwcP6IvLDtZtnQ+3f/hUPo3TGN5+dtZOrCLbz45SZGds/m8jE9OGdIF1IS412sWqR1+dYRjFMSDC2PBxY8Be/fDfGJcOF/YMj3nEHQEnWCmTX0VuAeYAfgG+RggaEhqEtERCJUOLW4RaIjvX9/uSib35wziNcWbmHKvI38/JUl/P7NlVw8uhuXHdeD7u3buFm+SKuwGiMYeoWbYfrN8O0nzkQw5z0MmbluVyUtyNjmTI0GGGPWAsdZa/eEtqTmGT16tF2wYIHbZYiIiLQaay2fr9vDc19s5P1VO/BYy0n9OnLFmB6cMiBHU+tL1NpeVMqY+2fzpwuG8IPjurtdTmSzFhZPgXfvdNYFHP9HGHmVWgEjlDEm31o7ujHHBtM1dDNQFMT5IiIiEgRjDGP7dmBs3w5sLyrlxS838dJXm/jRswvIy07lB8d15+LR3eiYkex2qSIhVVW9jqDLhUSipa/A7PugaAtkdHHG/m1fCj3Gwfn/hrY93a5QWkkwQXA98LEx5i2gzLfzMGsDioiISAvpnJXCz844iltO7csHK3fw3LyNPDhrDf/84GvOGtyFK8b04JiebUO26L2Im3yzhup6bqKlr8DMn0CFd0Kq4gJnG3IxXPAExClZx5JgguAm75bk3URERMRlifFxnD2kC2cP6cLanQeYMn8jr+VvYeaSAvp3yuDyMd05f0QeGVqCQiKYrZ41VEGwSd6/pyYE+tv0hUJgDApm+YjfhbKQqOPf7J7VFU67G4Ze7HZVIiISQ/rmpHPPxKO5fXx/Zi4p4Ll5G/nt9BU88I6zOP3lY3owsEum22WKNJm6hjZB2QFY/RYsfclp/QukaEvr1iRhoclB0BjzT2vtT40xM6H+mrfW2vNCUlkkq9vsXrTZeQwKgyIi0uraJCVwyTHOeMElW4p4fp7TSjhl/iZG92jLFcf34KzBnUlO0BIUEhk8mjX08Koq4duPYcnLsPpNqDgEWd2dheHL9tc/Pqtrq5co7mtOi+Bz3tu/hrKQqDL7vvrN7hUlMOvX0HkoZHSClGzNxnQkalUVEQkpYwzDu2UzvFs2d507kNfyt/D8vI3c9tJi2qclcfEx3fjBsd3p1k5LUEh40xjBAKyFbUucz0/LX4MDOyAly/nsNPRS6Hacs9+/sQIgMdX5jCUxpzlBcBeAtfaTENcSPRpqXj+4Cx49zrmfkALpOc5sTemdIKNzzW1GZ0j33qa2i80+22pVDZ6CtIgcRnabJH50Ym9+OLYXc9ft5rkvNvLEJ+t4/JN1nNI/h8vHdOc7R2kJCglP3hxIvIIgFG6CZa86rX+710BcIhw1HoZe4twm+M0a7PscoM8HQvOC4DRgJIAx5nVr7XdDW1IUyOrqBJe60jrCWQ9A8XYo3ub8paZ4O+xaDes/gbIAq3HEJfgFxc5Oa6Lv1j9EpnWEuCZ26WmNoFBR6nRBKN0PpUXO91i6v2Zfrdsi7zH7Ycdy8FTVea0SeOsXUH4Qsrs5XRyyukKS/nJdz9JXYMZPoFJBWkQOLy7OcGK/jpzYryMFhSW89OUmXvxqMz+cvICubVO57LgeXDy6K+3TtQSFhI+arqEuF+KWkkJYOd35/37jHGdf9+Nhwj9g0PnQpl3D5w69WJ8FBGjGgvLGmEXW2hF177strBaUr9uaBU6z+8SHDv+LV34IDmyH4h0N3Hq3kr31zzVxThhM9wZE/8CY3tlvXyeIT2xcjZVltUNadVgLFOSKAj9XVX7k9ys509lSMp0uDMmZ8M2sxr3XAG06OIHQFw6zu0FWN+++7pDaNnq64VrrvM8Hdjh/TCj2uy6q/7iwDfZtCHx+QgqMuNx5f7K7Q3YP5zatQ/S8R6GiFlWJYRVVHt5bsYPn5m1g3vq9JMXHcc6Qzlw+pgejerRl+uICHpy1hoLCEnKzU7l9fH/OH5HndtkSQ5ZvLWLCw3N44opRjD+6s9vltI7Kclj7Pix9Gda8C1Vl0L6v0+1z6Pe0/p8ALb+gvG3gvvg0t9k9qQ206+1sh1NZ7nzg97Uo+odEXxDYttjpimo99c9v094Jap6K2vsrSmDajc5YxtL9zj8wR5KU4QQ4X5BL6+j8o+S/L9kv4KXUuZ+UEbjr6z8GB25VzeoK17zrPFe0xekOUbQZCjfDrjXwzQc1rWA+iWnecNjVG4D8A2NXJyQ3tTU11Kx1grMv0BX7BT3/n2/x9vrfHzjvo6+VuOsxDQfBylJY/jqU7Ku9PyHVGwx9AbF7TVDM6uZ0Y46loKiuyRLjEuPjOHdoF84d2oVvdhQzZf4mXs/fwrTFBXTJTGb3wXIqqpyPAFsLS7hz6jIAhUFpNTGzfIS1sPlLJ/ytmOr8/92mA4y+xvn/KHdkbP3/LCHVnCA4zBizHzBAqvc+3sfWWqt5qKFlm90Tkrwf2Lsd/riqSicMBmpdXPB04HM8lTDgXL+wllUn0PkHu4yWC1Cn3d3AYOZ7Dv+9WwuH9kLRJicc1g2MWxfWb1GNS4DM3Jqupr4WRf+WxcTU+l+rMS1G1QEvULCrE/gCBrx077jRLpA3qua+byypr3twcnrt8zbNayBId4OfLXeCftFm530p3AyFG733NwV+jxJS/FoRA2xpOZE5lrWiFMqK/Vq9vfffuSPwhE+z71MQlJjTr1MG9553NL86qz/TFxfw22nLqfTU/jtwSUUV98xYDkC7tKRaW0qiZiKV0Iv6rqF71jnhb+nLzh93E1Kdz2dDL4E+pzi9u0SC1OSuoeEqrLqGRoIGW9y8QSEctFTXvPKD3nC42S8wbqlpWSwuqN+Smtaxdjg8uAtWvFG762tcIvT6jhOW/btqVhyqX0NiGmR2qZkUyD/Y+Y8HTc5o3vfY3O7JPmUH/ILiJr+g6N13aHft4+OT/QJ099rdTrO7O2G1blAM5udrLZQfqOmCXFZc00W5+r6vi3Jx7e7N/s83putyXYPOh06DofNg6HS08z3rr7ESQ3r931tN6g6UlhRPu/Qk2qUl075OSGyXllS9r31aMu3Sk0hLig96Jshpi7aq62qUW7hpHxc++jnPXHMMp/TPcbuc0Di4G5ZPdcLf1gWAgd7fccLfgAnO5wuRI2jprqESDRpscQuj6YNbqlU1KQ069ne2QKoqYH9B7XBYtMl5vGMlfD3L6WJZl6cC1s12uvZmdIG8kXWCnd9YzeYGvMYKdlaw5HTIGehsgZQfrAmFvpDoC45r3naCsr/4pJoxm9ndnSC25u2aIFa0GabfDOs+dLoWV4e3ukHPu6+8OHC351pM7Vbs5AwnkLbv592XUbt1u/rYDHjhEifI15WQ6nS7XjnN773KdAJh9TbEed/qttKKRInc7FS2FtbvwdAlK4Xnf3Qcew+W19r2HChn78Ey9hwsZ8f+UlZt28+eg+WUVwb+HU5KiKNdG284TK8bGJNpl5bovXX2ZaUmEufXLDRt0VbunLqMkgpnwjF1XY1ONlrWEawocf4/XPoKrP3A6ZnVaTCc8XsYcpHTY0mkhSgIxipNH9yw+ERo28PZArEWfteWBofI/mRhi5XWJC3ZPTkpDXIGOFsg5YfqtCj6bWvehYM7659TVQ5LXnTuxyfVD3HtetXcr9VVOQOSs+qEu0yn1bW53VXPuO/wLaplxbBzlTO77Y4VsH25M213eXHN8W17eYOhX+thds/I7EIr4uf28f1rBS2A1MR47jhrAH06ptOn45Ffw1rLofIqJyge9AbFA97weKicvd77ew6Ws3HPIfYeLOdAWWXA14qPM7Rtk0i7tCTatkliyZZCSitqh8ySiioenLVGQTCKVHl/xBHZNdTjgQ2fOeFv5XTn/46MLjDmJqf1r/NgtyuUGKEgGMs0fXDzGNPwEiFZXVu/nnCU1Obwra73ZhM4SBu4a0ftNY/ccKQ/lCRnQLdjnc3HWifo7ljh3ZY72+q3qP5eE9Og06CagNhpsPM4JatVvz2RYPjCVDBdL40xpCUnkJac0OjF60srqth3KFBrY02Y3Hewol4I9CkI0Iopkcs3RjAs1xFsaOjDjpWw9CVY9hrs3+pM9DboPCf89Rzn/sR1EnMUBEWaIxK61oazwwVpt0OgT1P/UGJMTUvygHNq9pcfgl2ragLi9uWwYhrkT645Jqt7TdfSzt6A2K734T8UaHkLcdH5I/JavXUtJTGeLlmpdMkKMHmXn7EPfBiw62rnrJSWKk1c4AuCwY4nDblAs05Puwk++B3s3wImHvqeDmf+Ho46W2shi6sUBEWaQ11rgxNLQTqpjTPja96omn3WOuNQ/VsOd6yAb94D6+1ul5DijDWs1Xp4tLNIsJa3EGlQoK6rACXlVSzfWsTgPLXARwOPt+E3Ptz6hs6+r/6s054KZ0jE2X+Boy+E9Eb0nxZpBQqCIs2lrrXNF+tB2hjIynO2o86s2V9RCrvX1LQc7lgOa96BRc/XHJOR6yzvUXfCIi1vIQIE7rp6yTFdefHLzXz3sc/54wVDuGiUuvFHurBcPmLfhsC9XcCZiO6461u1HJEjURAUEXcoSNeXmAJdhjmbj7VwYGdNq+GO5c7U4oEUbYZHjnVmpk332zI6Q3pOzePUtlryQqJaoK6rPziuB7e+sIhfvrqEpVsKuevcQSQlaPKmSBU2XUOrKp3eHAuegrWzGz5OcwhIGFIQFBEJZ8Y4wS6jE/Q9zdm38fPAf3VOSoeORznBcfOXzjqWgZY6iU/yhsIcZ2mT9By/sNjZGx47QVoOJCQ1r26NYZQw0yE9meeuPZa/zFrDk5+uZ0XBfh69bCSdMjV2MBJVTxbjVpPg/m2w6DlnvPf+rc6sn9+5A1Lbwex7YmPog0Q8BUERkUjT0BjLCf+oHbasddZePLATirc7wdC3FXtv922AzfPg0J7AXyu1XU0wPFwrY0pWTStjJIxhVFCNSQnxcfz6nIEM7ZrFr15byoSH5/DoZSM5pmc7t0uTJvK4sXyExwPffgILnnZmhLZV0PsUOPvPcNRZzvJTAG3a6t8XiQgKgiIikaaxYyyNcQJaShZ06Hf416yqcAJjoLDo2zZ+4dxWldU/PyGlJhhuXw6VdSZLqCiBd+5wWiMTkv1uk51Wx+rbpNr74pNCv/ZiJARVaVEThubSLyeD659bwPefnMdvJwziyuN7uN/NUBqtqjUXlD+0FxZPgQXPwN51zh/Ijr8JRl0D7fvUP15DHyRCKAiKiESiUH/QiE+smcDmcKyF0kK/VsadcGB77eBYNwT6lOyFV69qem1xiTXhMT7JLzgmBwiWSYH3+UJnfCLM/Vf9Wf002U7M6d85g+m3jOPnLy/mnhkrWLK5kD9eMITUJK3lFglsSwdBa50u9guehhVvOH8A6zbG6f45aJIzplskwikIiohI4xnjTDaT2hY69g98zD8GBx7DmNEZLnvd+UBVWe53Wx5gXxlUljktlfX2ldfc+u5XlkJpUe3n6h5jq+rX5K9oM7x9O+QMcpbryBkIyenBv2cStrJSE/nPlaN5+MO1/HP216zeXswTV4xq9CL34h6PkwND3mGAsmJnQq4FzziTcyVlwMgrnNa/zoND/MVE3KUgKCIiodXQGMYzfu/uBylPlRMIHxntTO5QV3wSLH4Byg/U7GvbE3KO9q7nOMi53643xOu/z2gRF2e47fR+DO2axW0vLWLiI3N46NIRnHSU1noLZ1XeJBgfqhbB7cvgq6dg2avOvwGdh8CEf8KQ7+kPQhK19D+ZiIiEVriuExkXD0lt4PR7AwfViQ/B4IugaJN3qY6VsHOFc//rd8B6Z6dISHFaQzsN9rYeelsQ03Pc+K4kRE4ZkMOMW8Zxw/P5XPXMl/zyzP7cdHIfjRsMUyFZPqKiBFZMc5Z+2PKV87s9+Lsw+lrIG6lldiTqKQiKiEjohfNkCUcKqm17OtuAc2vOqSiBXWtg50pvSFwBaz9wJpDwadPB23Lo3XIGQccBTviUiNCzQxpTbzqBO15fxoOz1rB0SyF//d4wMlIS3S5N6rC+rqHNyWq71zpj/xZPccY8t+8H4++H4d93ur2LxAgFQRERiT1NDaqJqZA73Nn8HdzthMKdK53xRDtWOmOLqifMMc6sgr5xh50GOffb9jr84CYtb+GaNkkJPHTpcIZ3y+ZPb69i0r/n8uQVo+ibk+F2aeKnumtoY5NgVYWz5MOCp+DbTyEuAQZOhNE/hJ4nqvVPYpKCoIiISHOldYDe33E2H0+Vsz6jr+Vw5wonJK6aCXibMRLTIGeAt+XQrxWxTTstbxEGjDFcO64XR+dmcssLC5n0yFz+dvFwzhrc2e3SxMvT2FlDCzfDwv/BwmedWY2zusOpv4URVzjro4rEMAVBERGRUIqLd1oB2/eBQefV7C8/CLtW14w/3LHcaaFY+GzNMemdna5qlaW1X1PLW7hiTO/2zLx1HDc+v5Abns/nxpP78Msz+ze+FUpaTM0YwUBPVsHa2U73z29mOf1IjxrvtP71Pd35HRURjG8dlkg3evRou2DBArfLEBERaTxrnbUYdyyvGX+45MWGjx/yPadbabve3q0XpHVUt7YWVlZZxb0zVvLil5s4sV8HHrp0BG3TktwuK3YtfYWDb99Nask2PJldSTjjHuePJAd2wqLnIH8yFG6CtBwYeSWMugqyu7tdtUirMMbkW2tHN+pYBUEREZEw0tA6jAnJToth0eaaGUwBktKdQFg3ILbrDRm5LbDQWux66ctN3D19BR0zknniilEMzstyu6TYU7frNEB8srPcw7Yl4Klwxvwdcy30PxcSFNgltigIioiIRKpAH3R9y1sMvRgqy53Wjn3fwt713s17f98G54OwT3yyMwNqrYDoDYlZ3SBes2E21ZLNhdzwfD57D5bzpwuG8N1RXd0uKXZUlsNDw2B/QYAnDYy5CUZdDR2Pau3KRMKGgqCIiEgka+6soZ4q2L+1fkDc+60THCsO1Rxr4p3ucv4tiO16Oy2LbXtCYkro64sSuw+UcesLi/hi/R6uPL4Hd507iKSEKGp5bamfb2U5lBb5bYV1Hh9hqyw5zIsbuLcw+BpFIpyCoIiIiNRmrTNrYr2Q6L1fVuR3sIHMvNotiL6up9sWwzu/arjFMkZUVnn4y6w1PPnpekb1aMujl42kU+ZhwnOkOFyL9KDzWzDI4SzpkJJ1+O3zR5yvW1dWN/jZ8tC+FyIRKOyDoDHmaWACsNNaOzjA8ycD04FvvbumWmvvO9xrKgiKiIg0k7VQsq9+QPR1Pz2468ivkZINE/4ObTpAm/Y1W5SP0Zq5pIA7Xl9KWnICj142kmN6tnO7pOA0NEYVQ/XyJw0x8ZCafYQwd5jnE9sceeKjI3WdFolxkRAETwIOAM8eJgj+0lo7obGvqSAoIiLSQsqKawLiq1c17dzkTGd9xDbt/UKi93FandDYpr0TFIKd4KaVu66u2V7M9c8tYMu+Eu6eOIgrxvTARNpMrjtWwvLX4LO/NXzMqXcFH+RCYekrFL91N2ml2yErj7jT71EIFPFqShB0ZR1Ba+2nxpiebnxtERERaaLkDOgy1Nne6xa4xSgzFy6fCof2wMHdzu2hvd5b7+MD250lMg7trr9Woo+Jg1T/oFg3RHq3NL/7/gGkbotR0WbnMbRYWOjfOYPpt4zj5y8v5u7pK1i8uZA/XTCElMQwX69u77ew/HVn27nSee8TkqGyrP6xWd3gpNtbv8ZAhl7MlH2jeOCd1ay4eTxpyVoWW6Q5wvk353hjzBKgAKd1cEXdA4wx1wHXAXTvrvVhREREWtxpdwfumnf67yBnYONfp/xQ7ZDoC43VIdK77f4GDs1z7vsvm+EvIcUbFNvB7jX1g0xFidNC2IKtRlmpifznytE8/OFa/jn7a9ZsL+bxy0fRrV2bFvuazVK8HVa8Acteg63enlTdxsA5f3XGAK7/KPDP97S73am3Ab4F5ePjIqzlVSSMhGsQXAj0sNYeMMacA0wD+tU9yFr7JPAkOF1DW7dEERGRGOQLU8F2vUxq42zZ3Rp3vMfjTBJSt5XRtx303m5fGvj8os2w5CXofTJkdG5arY0UF2e47fR+DOmayW0vLWbiI3N46NIRnHRUxxb5eo1Wsg9WznC6fm6Y4wTqzkOc8D74wtqLrYfq59vCfCObIq0Hrkg4cW3WUG/X0DcDjREMcOwGYLS1dndDx2iMoIiIiDQ42YmJq2lR7DjQCYR9ToEeJzhdX0Nsw+6D3PB8Pmt2FPPLM/tz08l9WnfcYPlBWPOO0/K39gNnfcl2fWDIRTD4u9Cxf+vV0gIemv0Nf3//a77549kkxkfR0h0iQQr7MYJHYozpDOyw1lpjzLFAHLDH5bJEREQk3DXUdXXCvyBnAKz7CNZ/DPnPwPzHnCULuh4DvU9xwmHeSIhPDLqMnh3SmHrTCdzx+jIenLWGpVsK+ev3hpGREvxrN6iyDNbOdlr+1rzjrBuZkQvHXe8EwC7Do6YJrbpraJR8PyJucCUIGmNeBE4GOhhjtgD3AIkA1trHgYuAG40xlUAJcKmNlgUPRUREpOUcqWtjl2Ew7qdQUQqb5zmhcP3H8PH98PGfICkDep3ohMLeJ0OHo5odntokJfDQpcMZ1jWL+99Zzfn/nssTV4ymb0560N9mNU8VbPjMaflbNcNZry+1HQy7FAZfBN2PD34W1jDkUddQkaBpQXkRERGRQ3vh209rguE+71LGGbk1obD3yZDRqVkv/8W6PdzywkLKKj18b3RX3luxg4LCEnKzU7l9fH/OH5HX+BezFrYscFr+VrwBB3ZAUjoMmOC0/PU+OSStmuFq2qKt/HbacorLKslrzvsnEsXCfh3BlqAgKCIiIiGzb0NNKFz/CZTsdfbnDPKGQt/4wsa37m0rKuHiJ75g896SWvtTE+O5/8IhRw4zO1Y4LX/LX4fCjRCfDEed6bT8HTXe6QIb5aYt2sqdU5dRUlFVva/R759IDFAQFBEREQkVj8eZjXT9x87yChu/gKoy7/jCY51JZ3qfDLkjIf7wo25OuH82BUX111DMy05l7v+dWv+Eveud4Lfsddi1Cky887WGXAQDznUWco8hYx/4kK2FJfX2N/j+icSYiJ8sRkRERCRsxMVB7nBnG/dTZyKazfNrJp756E/w0R8hORN6+o8v7FdvENu2ACEQoMA/3Ozf5nT5XP4abM139nU/vmatv3SXl6NwUUGAEHi4/SLSMAVBERERkaZITK0Je+AdX/iJEwrXfQRr3nL2Z+bVHl+YnkNudiqj9r/PrxJeIdfspsB24C+VF7MkeTTkT3a6fm6YA1joPBTOuA+OvrDx6y1GMY/Hkp6SQHFpZb3ncrOjv1usSKipa6iIiIhIKO39tqYb6befOgu6A+QczXba0XbHFySbmjBTZQ0WSDAW264vZshFTtfPDv1cKT8cFR4q56cvL+bjNbuIN4Yqv8+vGiMoUkNdQ0VERETc0q6Xs42+xlnewTe+cN1HdP72E6iz5EG8sZTGtWFSyW9okziCf48aRU5miiulh6PlW4u44fl8duwv5ffnDyY9KZ6/vvd182ddFRFALYIiIiIirefebCDQZy/DjAtWcsdrS8lISeCxy0cyqke71q4u7LyyYDO/nbacdmlJ/PuykYzs3tbtkkTCWlNaBKNvhVERERGRcJXVtcH95w3L5Y2bTyA1KZ5Ln5zHc19sIFr+YN9UpRVV3Dl1Gb96bSmjerRl5q3jFAJFQkxBUERERKS1nHZ3/fX+ElOd/cCAzpnMuGUcJ/bryG+nr+D215ZS6rdmXizYsu8QFz/xBS9+uYkbT+7Dsz88lg7pyW6XJRJ1NEZQREREpLUMvdi5nX0fFG1xWghPu7tmP5CVmsh/rxzNv2Z/w79mf8Pq7ft5/PJRdG3bxqWiW8+nX+/itpcWUVlleeKKUYw/urPbJYlELY0RFBEREQlTs1ft4KcvLyYhzvDw90cyrl8Ht0tqER6P5dGP1/K397/mqJwMHrt8JL07prtdlkjE0RhBERERkShw2sBOzLhlHB0zkrny6fk8/sm6qBs3WFRSwXXPLeCv731dPU5SIVCk5SkIioiIiISxXh3SeOOmsZw9pAsPvLOam6Ys5EBZ/UXVI9HKgv2c98gcPl6zi3snDuKflwynTZJGLom0Bv2miYiIiIS5tOQEHvn+CIZ3zeb+d1bxzc4DPHHFKPpEcMvZ6/lb+M20ZWSlJvLy9WO0XIZIK1OLoIiIiEgEMMbw45N68/y1x7H3YDmTHpnLrBXb3S6rycoqq7hr2jJ+8eoShnXN5s1bT1QIFHGBgqCIiIhIBDmhbwdm3jqO3h3TuP65fP46aw1VnsgYN7itqIRLnpjH8/M2cd1JvZnyo+PomKGlIUTcoCAoIiIiEmHyslN55frjuXh0Vx75aC0/nPwVhYfK3S7rsD5fu5sJD83hmx3FPHrZSH59zkAS4vVRVMQt+u0TERERiUApifH8+btD+dMFQ/h83W4mPjKHlQX73S6rHmstj328jsufmk/btCSm3zKOc4bPKO/UAAAOPklEQVR0cbsskZinICgiIiISoYwx/OC47rx8/fGUV3q48LG5TFu01e2yqu0vreD65/L587urOXtIF6bfPJa+OZE7wY1INFEQFBEREYlwI7u35c1bT2Ro12x++vJifjdzBRVVHldrWrO9mEmPzGX26p3cde5AHvn+CNKSNWG9SLhQEBQRERGJAh0zkpnyo+P44dhePDN3A5f9Zz47i0tdqWX64q2c/++5HCir5MUfj+FHJ/bGGONKLSISmIKgiIiISJRIjI/jbu/C7Eu3FjLx4Tnkb9zXal+/vNLDvTNWcNtLixmcl8lbt47j2F5aGkIkHCkIioiIiESZ80fkMfXGsSQlxHHpk18wZf5GrG3ZJSa2F5Xy/f/MY/LnG7h2XC9e+PEYcjJTWvRrikjzKQiKiIiIRKFBuZnMvGUcJ/TpwG/eWM4dry+ltKKqRb7WF+v2MOHhz1i1bT8Pf38Ev50wiEQtDSES1vQbKiIiIhKlstsk8fTVx3DrqX15ZcEWLn7iC7YWloTs9a21/OfT9Vz+1HwyUxOZfvNYJg7LDdnri0jLURAUERERiWLxcYZfnNmfJ68YxfpdB5n48Bw+X7s76Nc9UFbJzS8s5I9vr+KMgZ2YfvNY+nXKCEHFItIaFARFREREYsCZR3dm+i1jaZeWxOVPzefJT9c1e9zg2p3FTHpkDu8u386vzxnAY5ePJCMlMcQVi0hLUhAUERERiRF9OqYz7eaxjD+6M396ezW3vLiIg2WVTXqNN5cWcN4jcykqqWDKj8Zw3Ul9tDSESARSEBQRERGJIenJCTx62Uj+7+wBvLNsGxc8Opdvdx884nkVVR5+/+ZKbnlhEQM6Z/DmrSdyfJ/2rVCxiLQEBUERERGRGGOM4Ybv9OHZHx7HruIyznt4Dh+s3NHg8Tv3l3LZf+bz1JxvufqEnrx03fF0ztLSECKRTEFQREREJEaN69eBGbeMo0eHNvzo2QX8/f2v8Xhqjxv8asNezn14Dsu2FvGvS4dz73lHk5Sgj5AikS7B7QJERERExD3d2rXhtRtO4DdvLOeh2d/w/ortFJZUsL2olMzUBPaXVNKjfRueu/ZYBnTOdLtcEQkRBUERERGRGJeSGM9fvzcUg+W1hVur9xeVVBJn4Prv9FYIFIkyatcXEREREYwxfLF+b739HguPfLjOhYpEpCUpCIqIiIgIAAWFJU3aLyKRS0FQRERERADIzU5t0n4RiVwKgiIiIiICwO3j+5OaGF9rX2piPLeP7+9SRSLSUjRZjIiIiIgAcP6IPAAenLWGgsIScrNTuX18/+r9IhI9FARFREREpNr5I/IU/ERigCtdQ40xTxtjdhpjljfwvDHGPGSMWWuMWWqMGdnaNYqIiIiIiEQrt8YITgbOOszzZwP9vNt1wGOtUJOIiIiIiEhMcCUIWms/BeovVFNjEvCsdcwDso0xXVqnOhERERERkegWrmME84DNfo+3ePdt8z/IGHMdToshwAFjzJojvG4WUNTIGhp77JGO6wDsbuTXjGRNeW8jtYZQvX6wr9PU89247iE2rn1d9633Wi153Tf2eF33jnC47qFl64jU676p5+i6b7xYuO5D+fqRft035rhwve57NPpIa60rG9ATWN7Ac28B4/wezwZGheBrPhnqY490HLDArfe4lX+ejX5vI7WGUL1+sK/T1PPduO69x0T9ta/rvvVeqyWv+8Yer+s+9NdEuNYRqdd9U8/Rde/ONRHOdYTDZ51wuO4bc1w0XPfhuo7gFqCb3+OuQEEIXndmCxzblNeMZuHwPrR0DaF6/WBfp6nn67pvOeHwPkTKdR/sa7Xkdd/Y48Ph5x0OwuV9aMk6IvW6b+o5uu4bL1zeh0j5Nz/Sr/vm1hFRjDfRtv4XNqYn8Ka1dnCA584FbgHOAY4DHrLWHtuqBYaIMWaBtXa023WItDZd+xKLdN1LLNJ1L7EoGq57V8YIGmNeBE4GOhhjtgD3AIkA1trHgbdxQuBa4BBwjRt1hsiTbhcg4hJd+xKLdN1LLNJ1L7Eo4q9711oERURERERExB3hOkZQREREREREWoiCoIiIiIiISIxREBQREREREYkxCoIuM8akGWPyjTET3K5FpDUYYwYaYx43xrxmjLnR7XpEWosx5nxjzH+MMdONMWe6XY9IazDG9DbGPGWMec3tWkRakvcz/f+8/85f5nY9jaEg2EzGmKeNMTuNMcvr7D/LGLPGGLPWGPN/jXipO4BXWqZKkdAKxXVvrV1lrb0BuBiI6GmXJXaE6NqfZq39MXA1cEkLlisSEiG67tdba69t2UpFWkYTfwcuBF7z/jt/XqsX2wwKgs03GTjLf4cxJh74N3A2MAj4vjFmkDFmiDHmzTpbjjHmdGAlsKO1ixdppskEed17zzkPmAPMbt3yRZptMiG49r3u8p4nEu4mE7rrXiQSTaaRvwNAV2Cz97CqVqyx2VxZRzAaWGs/Ncb0rLP7WGCttXY9gDHmJWCStfZ+oF7XT2PMKUAazkVUYox521rradHCRYIQiuve+zozgBnGmLeAF1quYpHQCNG/+QZ4AHjHWruwZSsWCV6o/s0XiVRN+R0AtuCEwcVESGObgmBo5VHzlwBwLojjGjrYWvsbAGPM1cBuhUCJUE267o0xJ+N0n0gG3m7RykRaVpOufeBW4HQgyxjT11r7eEsWJ9JCmvpvfnvgj8AIY8yd3sAoEska+h14CHjEGHMuMNONwppKQTC0TIB99kgnWWsnh74UkVbTpOveWvsx8HFLFSPSipp67T+E80FBJJI19brfA9zQcuWItLqAvwPW2oPANa1dTDAiotkygmwBuvk97goUuFSLSGvRdS+xSte+xCJd9xLrouZ3QEEwtL4C+hljehljkoBL+f927j5Uz7qO4/j7Y2qBq82yZBpzQWJPy6PlqGnr9M+ISKNmDIJSKso/CizsAQKfBtkDZQ9ShilZUM6pxRrColpO96BSZ24zwz/SwERqGMP9o7i+/XH9brq5vc+5z45Hzrb7/YLD9XBf1/f3+133df/xPd/rd8GmBe6T9FLzvte48t7XOPK+17g7Zn4DJoJzlORXwE7grCRPJPlUVT0PfA7YAjwC3F5VDy9kP6X55H2vceW9r3Hkfa9xd6z/BlI1cgqbJEmSJOkYYkVQkiRJksaMiaAkSZIkjRkTQUmSJEkaMyaCkiRJkjRmTAQlSZIkacyYCEqSJEnSmDERlCTNmyTXJ7m8b3tLkp/2bX8nyRdHxNgxi3YeT3LKkP2TSVZNc85FSb46Iu5pSe5o6xNJPnCY51+a5Ia2flmST4way6gxzDXOS6H1bfNC90OS9OIdv9AdkCQdU3YAHwW+l+Q44BTgVX2frwIuH3ZiT1UNTeRmaRI42PoxGHcTsGlE208CF7fNCeCdwN2zPX8g1o2zPXbAJH1jeBFxJEmalhVBSdJ82k6X7AG8FdgHPJPk5CQvB94MTAEk+VKSB5PsSXJNL0CSg215XJIfJXk4yeYkdye5uK+tzyf5S5K9Sd6UZDlwGfCFJLuTvKe/YwPVup8l+UGSHUn+3oubZHmSfUlOBK4F1rVY6wbOvzDJ/Ummkvw+yamDFyLJ1UmuaFXG3X1/h5KcMSzGsDH04rSYE0l2tWv26yQnt/1/SvLNJA8keXRw7O2YpUm2tbj7esckeX+7jg8l+UPbt7Jdm6m2PGtIvJOS3NK+w6kkH5r2rpAkHXFMBCVJ86ZV1J5PsowuIdwJ3A+8m666tqeqnkuyBjgTWElXeXtHktUD4T4CLAdWAJ9uMfrtr6pzgR8DV1TV48CNwPVVNVFV947o7lLgAuCDwDcGxvEccCWwocXaMHDufcC7quoc4Dbgy9M1UlVPthgTwE3AnVX1j2ExZjGGnwNfqaq3A3uBq/o+O76qVtJVXK/ihT4GbGn9OBvYneS1rU9rq+psumouwN+A1a1vVwJfHxLva8Afq+o84H3At5OcNN11kCQdWXw0VJI033pVwVXAd4HT2/oB/v/I5pr2N9W2F9Elhtv64lwAbKyq/wJPJdk60M5dbflnuqTxcP2mxf7rsIreCK8HNiRZCpwIPDbqhCTn0yW0vWrdYcVIshhYUlX3tF23Ahv7Dum/HsuHhHgQuCXJCXRj351kEthWVY8BVNXT7djFwK1JzgQKOGFIvDXARb1qJfAKYBnwyEzjkCQdGawISpLm2w66xG8F3aOhu+iqeavokkSAANf1KmVV9caqunkgTka082xbHmJu/9h8tm99VFuDfgjcUFUrgM/SJUHTasnezcC6qjo4lxizMOP1qKptwGrgn8Av2gtoQpfoDVoPbK2qtwEXTtO30FUSe9/hsqoyCZSko4SJoCRpvm2ne9zy6ao61KpMS+iSwZ3tmC3AJ5MsAkhyepLXDcS5D1jb5gqeSvcSlVGeAV45D2MYFWsxXUIFcMlMQVoF7na6RzofnUWMoe1W1QHgP33z/z4O3DN43Az9OAP4V1XdRJeUnkv3fbw3yRvaMa8e0rdLpwm5hW6eZtq558y2L5KkhWciKEmab3vp3ha6a2DfgaraD1BVvwN+CexMshe4gxcmP3cCT9BVFX9CN9fwwIi2fwt8eNjLYuZgK/CW3stiBj67GtiY5F5g/4g4q4DzgGv6Xhhz2gwxZhrDJXRz8fbQza289jDGM0k3L3AKWAt8v6r+DXwGuCvJQ0BvLuS3gOuSbAdeNk289XSPjO5Jsq9tS5KOEqka9kSIJEkLL8miqjqY5DXAA8D5VfXUQvdLkqSjnS+LkSQdyTYnWUL3MpX1JoGSJM0PK4KSJEmSNGacIyhJkiRJY8ZEUJIkSZLGjImgJEmSJI0ZE0FJkiRJGjMmgpIkSZI0ZkwEJUmSJGnM/A/wS1Jc2VHcDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results of weight scale experiment\n",
    "best_train_accs, bn_best_train_accs = [], []\n",
    "best_val_accs, bn_best_val_accs = [], []\n",
    "final_train_loss, bn_final_train_loss = [], []\n",
    "\n",
    "for ws in weight_scales:\n",
    "  best_train_accs.append(max(solvers_ws[ws].train_acc_history))\n",
    "  bn_best_train_accs.append(max(bn_solvers_ws[ws].train_acc_history))\n",
    "  \n",
    "  best_val_accs.append(max(solvers_ws[ws].val_acc_history))\n",
    "  bn_best_val_accs.append(max(bn_solvers_ws[ws].val_acc_history))\n",
    "  \n",
    "  final_train_loss.append(np.mean(solvers_ws[ws].loss_history[-100:]))\n",
    "  bn_final_train_loss.append(np.mean(bn_solvers_ws[ws].loss_history[-100:]))\n",
    "  \n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Best val accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best val accuracy')\n",
    "plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Best train accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best training accuracy')\n",
    "plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Final training loss vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Final training loss')\n",
    "plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "plt.gca().set_ylim(1.0, 3.5)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 1:\n",
    "Describe the results of this experiment. How does the scale of weight initialization affect models with/without batch normalization differently, and why?\n",
    "\n",
    "## Answer:\n",
    "BN 能够提升模型对初始权重的鲁棒性  \n",
    "原因：规范化操作带来了权重伸缩不变性，即权重的等比例变化会被 BN 消除"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch normalization and batch size\n",
    "We will now run a small experiment to study the interaction of batch normalization and batch size.\n",
    "\n",
    "The first cell will train 6-layer networks both with and without batch normalization using different batch sizes. The second layer will plot training accuracy and validation set accuracy over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No normalization: batch size =  5\n",
      "Normalization: batch size =  5\n",
      "Normalization: batch size =  10\n",
      "Normalization: batch size =  50\n"
     ]
    }
   ],
   "source": [
    "def run_batchsize_experiments(normalization_mode):\n",
    "    np.random.seed(231)\n",
    "    # Try training a very deep net with batchnorm\n",
    "    hidden_dims = [100, 100, 100, 100, 100]\n",
    "    num_train = 1000\n",
    "    small_data = {\n",
    "      'X_train': data['X_train'][:num_train],\n",
    "      'y_train': data['y_train'][:num_train],\n",
    "      'X_val': data['X_val'],\n",
    "      'y_val': data['y_val'],\n",
    "    }\n",
    "    n_epochs=10\n",
    "    weight_scale = 2e-2\n",
    "    batch_sizes = [5,10,50]\n",
    "    lr = 10**(-3.5)\n",
    "    solver_bsize = batch_sizes[0]\n",
    "\n",
    "    print('No normalization: batch size = ',solver_bsize)\n",
    "    model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "    solver = Solver(model, small_data,\n",
    "                    num_epochs=n_epochs, batch_size=solver_bsize,\n",
    "                    update_rule='adam',\n",
    "                    optim_config={\n",
    "                      'learning_rate': lr,\n",
    "                    },\n",
    "                    verbose=False)\n",
    "    solver.train()\n",
    "    \n",
    "    bn_solvers = []\n",
    "    for i in range(len(batch_sizes)):\n",
    "        b_size=batch_sizes[i]\n",
    "        print('Normalization: batch size = ',b_size)\n",
    "        bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=normalization_mode)\n",
    "        bn_solver = Solver(bn_model, small_data,\n",
    "                        num_epochs=n_epochs, batch_size=b_size,\n",
    "                        update_rule='adam',\n",
    "                        optim_config={\n",
    "                          'learning_rate': lr,\n",
    "                        },\n",
    "                        verbose=False)\n",
    "        bn_solver.train()\n",
    "        bn_solvers.append(bn_solver)\n",
    "        \n",
    "    return bn_solvers, solver, batch_sizes\n",
    "\n",
    "batch_sizes = [5,10,50]\n",
    "bn_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('batchnorm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plot_training_history('Training accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "plt.subplot(2, 1, 2)\n",
    "plot_training_history('Validation accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 2:\n",
    "Describe the results of this experiment. What does this imply about the relationship between batch normalization and batch size? Why is this relationship observed?\n",
    "\n",
    "## Answer:\n",
    "batch 越大，均值和方差估计越准确，BN 效果越好。所以硬件允许的情况下，batch size越大越好。  \n",
    "另一方面，batch 内和 batch 间的数据应尽可能随机（充分洗牌，正态分布），否则 BN 效果会比较差\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization\n",
    "Batch normalization has proved to be effective in making networks easier to train, but the dependency on batch size makes it less useful in complex networks which have a cap on the input batch size due to hardware limitations. \n",
    "\n",
    "Several alternatives to batch normalization have been proposed to mitigate this problem; one such technique is Layer Normalization [2]. Instead of normalizing over the batch, we normalize over the features. In other words, when using Layer Normalization, each feature vector corresponding to a single datapoint is normalized based on the sum of all terms within that feature vector.\n",
    "\n",
    "[2] [Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. \"Layer Normalization.\" stat 1050 (2016): 21.](https://arxiv.org/pdf/1607.06450.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 3:\n",
    "Which of these data preprocessing steps is analogous to batch normalization, and which is analogous to layer normalization?\n",
    "\n",
    "1. Scaling each image in the dataset, so that the RGB channels for each row of pixels within an image sums up to 1.\n",
    "2. Scaling each image in the dataset, so that the RGB channels for all pixels within an image sums up to 1.  \n",
    "3. Subtracting the mean image of the dataset from each image in the dataset.\n",
    "4. Setting all RGB values to either 0 or 1 depending on a given threshold.\n",
    "\n",
    "## Answer:\n",
    "1、2、4 类似 LN  \n",
    "3 类似 BN\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization: Implementation\n",
    "\n",
    "Now you'll implement layer normalization. This step should be relatively straightforward, as conceptually the implementation is almost identical to that of batch normalization. One significant difference though is that for layer normalization, we do not keep track of the moving moments, and the testing phase is identical to the training phase, where the mean and variance are directly calculated per datapoint.\n",
    "\n",
    "Here's what you need to do:\n",
    "\n",
    "* In `cs231n/layers.py`, implement the forward pass for layer normalization in the function `layernorm_backward`. \n",
    "\n",
    "Run the cell below to check your results.\n",
    "* In `cs231n/layers.py`, implement the backward pass for layer normalization in the function `layernorm_backward`. \n",
    "\n",
    "Run the second cell below to check your results.\n",
    "* Modify `cs231n/classifiers/fc_net.py` to add layer normalization to the `FullyConnectedNet`. When the `normalization` flag is set to `\"layernorm\"` in the constructor, you should insert a layer normalization layer before each ReLU nonlinearity. \n",
    "\n",
    "Run the third cell below to run the batch size experiment on layer normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before layer normalization:\n",
      "  means:  [-59.06673243 -47.60782686 -43.31137368 -26.40991744]\n",
      "  stds:   [10.07429373 28.39478981 35.28360729  4.01831507]\n",
      "\n",
      "After layer normalization (gamma=1, beta=0)\n",
      "--------------\n",
      "(4, 3)\n",
      "(4, 3)\n",
      "--------------\n",
      "  means:  [-4.81096644e-16  0.00000000e+00  7.40148683e-17 -5.55111512e-16]\n",
      "  stds:   [0.99999995 0.99999999 1.         0.99999969]\n",
      "\n",
      "After layer normalization (gamma= [3. 3. 3.] , beta= [5. 5. 5.] )\n",
      "--------------\n",
      "(4, 3)\n",
      "(4, 3)\n",
      "--------------\n",
      "  means:  [5. 5. 5. 5.]\n",
      "  stds:   [2.99999985 2.99999998 2.99999999 2.99999907]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after layer normalization   \n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 =4, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before layer normalization:')\n",
    "print_mean_std(a,axis=1)\n",
    "\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "# Means should be close to zero and stds close to one\n",
    "print('After layer normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=1)\n",
    "\n",
    "gamma = np.asarray([3.0,3.0,3.0])\n",
    "beta = np.asarray([5.0,5.0,5.0])\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "print('After layer normalization (gamma=', gamma, ', beta=', beta, ')')\n",
    "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  1.433615657860454e-09\n",
      "dgamma error:  4.519489546032799e-12\n",
      "dbeta error:  2.276445013433725e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "ln_param = {}\n",
    "fx = lambda x: layernorm_forward(x, gamma, beta, ln_param)[0]\n",
    "fg = lambda a: layernorm_forward(x, a, beta, ln_param)[0]\n",
    "fb = lambda b: layernorm_forward(x, gamma, b, ln_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = layernorm_forward(x, gamma, beta, ln_param)\n",
    "dx, dgamma, dbeta = layernorm_backward(dout, cache)\n",
    "\n",
    "#You should expect to see relative errors between 1e-12 and 1e-8\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization and batch size\n",
    "\n",
    "We will now run the previous batch size experiment with layer normalization instead of batch normalization. Compared to the previous experiment, you should see a markedly smaller influence of batch size on the training history!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No normalization: batch size =  5\n",
      "Normalization: batch size =  5\n",
      "Normalization: batch size =  10\n",
      "Normalization: batch size =  50\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ln_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('layernorm')\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plot_training_history('Training accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "plt.subplot(2, 1, 2)\n",
    "plot_training_history('Validation accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 4:\n",
    "When is layer normalization likely to not work well, and why?\n",
    "\n",
    "1. Using it in a very deep network\n",
    "2. Having a very small dimension of features\n",
    "3. Having a high regularization term\n",
    "\n",
    "\n",
    "## Answer:\n",
    "2: 直接影响均值和方差的计算  \n",
    "3: 高正则化会使 affine 层输出偏向于0而减弱 normalization 层的作用\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
